CN115100880B - Bus signal priority control method for realizing balanced distribution of buses - Google Patents
Bus signal priority control method for realizing balanced distribution of buses Download PDFInfo
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Abstract
The invention discloses a bus signal priority control method for realizing balanced distribution of buses, which comprises the following steps: dividing delay grades of buses reaching bus stops; setting the maximum adjustable amplitude of the intersection signal phase according to the delay level of the bus; mapping the position of the bus on the road network, and calculating the distance from the bus to the downstream intersection and the bus stop; predicting the time for a bus to arrive at a downstream bus stop; calculating the time interval of two adjacent buses in the same line reaching a bus stop; analyzing whether the bus is delayed or not and the delay level, and setting the bus priority; predicting the time of a bus reaching an intersection; and implementing bus signal priority control. The bus signal priority control method and device solve the problems that in the prior art, the target time of a bus arriving at each bus stop cannot be accurately calculated according to the schedule of the departure, and efficient bus signal priority control is difficult to achieve.
Description
Technical Field
The invention belongs to the technical field of intelligent transportation, and particularly relates to a bus signal priority control method for realizing balanced bus distribution.
Background
The existing bus signal priority control technology mainly aims at reducing signal delay of buses at intersections, and dynamically tracks the positions of the buses on a road network by combining electronic maps by means of detection technologies such as satellite positioning equipment or Radio Frequency Identification (RFID); when a bus is about to reach an intersection, the priority control of the bus signals is realized by signal regulation and control means such as prolonging green lights, shortening red lights and the like.
Further, according to the bus shift schedule, the bus stop time is used as a target for improving the bus punctuation rate, and the bus at the late point is given priority to pass. On the basis of guaranteeing the bus passing efficiency, the signal control frequency of the intersection is reduced, so that the influence on the passing of other vehicles caused by the implementation of bus priority control is reduced.
The bus priority control strategy aiming at reducing the signal delay of buses at a single intersection reduces the signal delay of buses at the single intersection, but the situation that the distance between adjacent buses is too close easily caused by that the buses cannot be guaranteed to implement priority in all conditions, so that the phenomenon that a plurality of buses continuously arrive at the same station occurs; in contrast, the problem that the interval between the front bus and the rear bus is too large, and the waiting time of passengers is prolonged may also occur. Meanwhile, the problems that when a plurality of buses arrive at an intersection at the same time, the priority among the buses is high in influence on other vehicles due to the fact that priority control is implemented at each intersection and the like cannot be effectively solved, and the overall operation benefit of the buses is influenced.
The bus signal priority control technology aiming at improving the standard point rate of buses reaching bus stops theoretically solves the problems of connection among buses and priority passing authority of multiple buses reaching an intersection at the same time. However, the calibration of the quasi-point state of the bus based on the bus shift schedule needs to accurately calculate the time when each bus arrives at a bus stop, and as the bus route is prolonged, uncertainty factors such as different driving habits, road condition differences along the route, traffic flow states and the like have great influence on calculation, so that the condition of large-scale bus signal priority control cannot be met.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a bus signal priority control method for realizing balanced bus distribution, so as to solve the problems that the target time of the bus reaching each bus stop cannot be accurately calculated according to the departure schedule in the prior art, and efficient bus signal priority control is difficult to realize. According to the method, the time when the vehicles arrive at the bus stops and the intersections is predicted through the deep neural network model, the delay state of the buses is analyzed based on the time interval when the adjacent vehicles arrive at the same bus stops on the same line, the signal priority control is only implemented for the buses in the delay state, and in the process of implementing the signal priority control, the two-stage control is optimized through the period scheme optimization and the phase real-time optimization based on the predicted time when the buses arrive at the intersections, so that the signal optimization efficiency is improved. The method reduces the influence on the operation of other vehicles while guaranteeing the bus operation efficiency, balances the waiting time of passengers and improves the overall bus operation benefit.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The invention discloses a bus signal priority control method for realizing balanced distribution of buses, which comprises the following steps:
1) Dividing delay grades of buses reaching bus stops;
2) Setting the maximum adjustable amplitude of the intersection signal phase according to the delay level of the bus;
3) Mapping the position of the bus on the road network, and calculating the distance from the bus to the downstream intersection and the bus stop;
4) Predicting the time for a bus to arrive at a downstream bus stop;
5) Calculating the time interval of two adjacent buses in the same line reaching a bus stop;
6) Analyzing whether the bus is delayed or not and the delay level, and setting the bus priority;
7) Predicting the time of a bus reaching an intersection;
8) And implementing bus signal priority control.
Further, the step 1) specifically includes: the delay state is classified into a slight delay, a general delay and a serious delay according to the delay time of predicting the arrival of the bus at the stop.
Further, the delay state is that the time interval of two adjacent buses on the same line reaching the same station is calculated, if the time interval exceeds the shift interval in the shift schedule, the delay state is judged to be that the next bus is delayed, and the delay grade of the next bus is divided according to the delay grade dividing table.
Further, the step 3) specifically includes: the method comprises the steps of collecting bus coordinates, speed and heading information in real time through vehicle-mounted positioning equipment, and calculating the position of a bus on a road network through a GIS space analysis method by combining high-precision road network data, bus route paths and bus stop information, wherein the steps include mapping the position of the bus on a road center line and calculating the distance from the vehicle to the end point of a road section.
Further, the GIS space analysis method calculates the position of the bus on the road network specifically as follows:
31 Constructing a road and road section two-stage data set according to the high-precision road network data and the bus line; extracting road data passing through a bus route, calculating an envelope rectangle of each road data, and constructing a road set containing road codes and road envelope rectangle fields; respectively constructing a road section set comprising road codes, road section codes and road section enveloping rectangles aiming at road sections contained by each road; the envelope rectangle is a boundary rectangle formed by minimum longitude and latitude coordinates and maximum longitude and latitude coordinates in boundary nodes of a road or a road section;
32 Positioning the road section of the bus, and selecting a road set corresponding to the road according to the road of the bus and the received bus positioning data; positioning a road where a vehicle is located according to whether the enveloping rectangle of the road contains vehicle coordinates or not; positioning a road section where a vehicle is located according to whether a road section enveloping rectangle corresponding to the road contains vehicle coordinates or not;
33 Mapping the position of the bus to the central line of the road, and marking the position of the bus on the road network; according to the obtained road section, respectively calculating the foot hanging and the foot hanging distance from the bus position to the subsection between each coordinate node, taking the subsection with the foot hanging on the subsection and the subsection with the minimum foot hanging distance, and calculating the coordinate from the bus positioning data to the foot hanging on the central line of the subsection, wherein the coordinate is the position of the bus mapped on the road network; the distance from the center line of the road to the corresponding intersection or the road section between the foot hanging parts mapped on the center line of the bus stop, namely the distance from the bus to the intersection or the bus stop.
Further, the step 4) specifically includes: according to the position of the bus on the road network obtained in the step 3), calculating the distance between the bus and the nearest two bus stops at the downstream, and predicting the time of the bus reaching the nearest two bus stops at the downstream in real time by combining the time interval and the road congestion length data through a deep neural network model, and recording as ST.
Further, in the step 4), the time of the bus reaching the downstream bus stop is predicted in real time through the deep neural network model, which specifically includes:
41 Programming a model training dataset: according to the bus stops along the bus route, records of historical buses arriving at each bus stop at different positions of the road network are collected, and the collected data comprise: the distance x 1 of the bus from the target point, the current period x 2, the length x 3 of a congestion road section from the bus position to the bus stop (0 when no congestion exists), and the time y taken by the bus from the current point to the bus stop; compiling an input set X= [ X 1,x2,x3 ] and a result set Y= [ Y ];
42 Splitting the collected data into a training set and a testing set, wherein the training set is used for model training, and the testing set is used for model verification; inputting the training set into a deep neural network model, training, and calculating model errors by using a test set after training;
43 Adjusting the number of neurons of two hidden layers in the deep neural network model, repeating training until the test error is less than 5%, and determining a final prediction model;
44 When new bus positioning information is received, extracting new bus distance from a bus stop, current time period and length data of a congestion road section along the way through the position of the new bus on the road network, inputting the new bus distance, the current time period and the length data of the congestion road section along the way into the final prediction model, and predicting the time of the new bus reaching the bus stop.
Further, the deep neural network model adopted in the step 42) is a deep neural network with a 4-layer network structure, and includes an input layer, two hidden layers and an output layer; in initial training, the input layer contains 3 neurons, and both hidden layers contain 128 neurons; the output layer contains a neuron, both the output layer and the hidden layer use ReLU as an activation function, and the output layer does not use an activation function.
Further, the step 5) specifically includes:
Traversing each bus stop in the road network, searching two buses closest to the bus stop according to a route, respectively marking as V 1、V2, wherein V 1 is closest to the bus stop, and respectively calculating the time interval between the arrival of a bus V 1 and a bus V 2 at the bus stop and the arrival of the last bus at the bus stop according to the following formula;
ΔST2＝STjm-STim
Wherein i is the serial number of the bus V 1 in the bus line; j is the serial number of the bus V 2 in the bus line; k is the serial number of the bus which arrives at the bus stop before; m is the serial number of a bus stop in a bus line; f m is the estimated bus stop arrival time of the first bus; ST im is the predicted time for the bus V 1 to arrive at the bus stop; ST jm is the predicted time for the bus V 2 to arrive at the bus stop; ST km is the time when the previous bus arrives at the bus stop; delta ST 1 is the time interval from the arrival of the previous bus at the bus stop when the bus V 1 arrives at the bus stop; Δst 2 is the time interval from arrival of bus V 1 at the bus stop when bus V 2 arrives at the bus stop.
Further, the step 6) specifically includes:
The bus shift interval is I, the bus with the time difference delta ST mark is traversed, and when delta ST is more than I, the bus arrival state is marked as late arrival, namely, the late time D=delta ST-I; when delta ST is approximately equal to I, the bus arrival state is marked as quasi-point arrival; when delta ST is less than I, the bus arrival state is marked as arriving in advance; setting delay levels of buses reaching late buses to the next bus stop according to a bus delay level dividing table, and opening priority passing permission of the buses; and closing the priority passing authority of the bus when the alignment point arrives or arrives in advance.
Further, the step 7) specifically includes:
according to the position of the bus on the road network obtained in the step 3), searching all intersections in 1000m from the downstream of the bus along the line, calculating the distance between the bus and each intersection, and predicting the time for the bus to reach the intersection in real time through a deep neural network model according to the time interval and the road congestion length data, and marking as CT.
Further, the step 8) specifically includes:
Traversing an intersection where buses with priority control authority arrive in two signal periods, searching a pre-configured intersection signal phase adjustable amplitude table under different bus delay levels, acquiring phase adjustment constraint, and setting phase constraint according to the highest delay level when a plurality of buses exist in different delay states of the same phase; according to the predicted bus arrival intersection time CT, the signal scheme of the current intersection is combined, two-stage control is optimized in real time through the period scheme optimization and the phase, and the delay time of the bus at the intersection is reduced.
Further, the period scheme optimization in the step 8) specifically includes: when the yellow lamp of the last phase of each signal period starts, starting a scheme optimization process of the next signal period; if the predicted time when the bus arrives at the intersection is within the bus passing phase release time, maintaining the original scheme unchanged, otherwise, optimizing the scheme according to different states under the condition of phase constraint; when the bus arrives before the traffic phase is started, shortening the phase duration before the traffic phase; if the bus arrives after the passing phase is finished, the passing phase and the duration of the front phase are prolonged, and the bus is ensured to directly pass through the intersection without stopping.
Further, the phase real-time optimization in the step 8) specifically includes: in the process of periodically executing signals at the intersection, dynamically monitoring the time when the bus arrives at the intersection in the period, and dynamically prolonging or shortening the phase duration under the condition of phase constraint when the bus arrives at the time and the arrival time predicted in the period optimization process deviate, so that the probability that the bus does not stop passing through the intersection is increased; when the bus fails to pass in the period, the time length of the remaining phase in the period is shortened, and the waiting time of the bus at an intersection is reduced.
The invention has the beneficial effects that:
On one hand, the time interval of the adjacent buses in the same bus route reaching the same bus stop is dynamically calculated by predicting the time of the buses reaching each bus stop, the running state of the buses is divided into three states of advance, quasi-arrival and late arrival, and the vehicles arriving at the late arrival are divided into three delay levels of slight, general and serious according to the delay time, so that the buses are given priority to implement priority control;
On the other hand, different phase adjustment amplitudes are respectively given to intersections according to different delay levels, so that the interference on other vehicle right of way in the process of preferentially implementing buses is reduced. Compared with the prior embodiment, the method shifts the calculation targets, dynamically predicts the arrival time interval of the adjacent vehicles by using a multi-layer neural network technology, reduces uncertainty factors and improves the stability of the system; by setting the priority of buses, the problem of priority authority when multiple buses arrive at an intersection at the same time is solved; the arrival time of the front buses and the rear buses at the bus stop is used as a control target, so that the maximum waiting time of passengers at the bus stop is reduced, and the riding experience is improved.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention.
Fig. 2 is a schematic diagram of a first shift arrival delay calculation.
Fig. 3 is a schematic diagram of a non-first shift vehicle arrival delay calculation.
Detailed Description
The invention will be further described with reference to examples and drawings, to which reference is made, but which are not intended to limit the scope of the invention.
Referring to fig. 1, the method for controlling bus signal priority for realizing balanced bus distribution comprises the following steps:
1) Dividing delay grades of buses reaching bus stops;
Dividing delay states into slight delay, general delay and serious delay according to the delay time of predicting arrival of the bus at a station, and specifically dividing a standard reference table 1; and the delay state is that the time interval of two adjacent buses on the same line reaching the same station is calculated, if the time interval exceeds the shift interval in the shift schedule, the delay state of the next bus is judged, and the delay grade of the next bus is divided according to the delay grade dividing table.
TABLE 1
| Delay time | < = 5 Minutes | < = 10 Minutes | 10 Minutes |
| Delay level | Slight delay | General delay | Severe delay of |
2) Setting the maximum adjustable amplitude of the intersection signal phase according to the delay level of the bus; different intersections can be set independently to accommodate different traffic flow characteristics, refer to table 2;
TABLE 2
| Delay level | Slight delay | Delay of time | Severe delay of |
| Amplitude of phase adjustment | 3 Seconds | 4 Seconds | 5 Seconds |
3) Mapping the position of the bus on the road network, and calculating the distance from the bus to the downstream intersection and the bus stop;
The method comprises the steps of collecting bus coordinates, speed and heading information in real time through vehicle-mounted positioning equipment, and calculating the position of a bus on a road network through a GIS space analysis method by combining high-precision road network data, bus route paths and bus stop information, wherein the steps include mapping the position of the bus on a road center line and calculating the distance from the vehicle to the end point of a road section.
Specifically, the GIS space analysis method calculates the position of the bus on the road network specifically as follows:
31 Constructing a road and road section two-stage data set according to the high-precision road network data and the bus line; extracting road data passing through a bus route, calculating an envelope rectangle of each road data, and constructing a road set containing road codes and road envelope rectangle fields; respectively constructing a road section set comprising road codes, road section codes and road section enveloping rectangles aiming at road sections contained by each road; the envelope rectangle is a boundary rectangle formed by minimum longitude and latitude coordinates and maximum longitude and latitude coordinates in boundary nodes of a road or a road section;
32 Positioning the road section of the bus, and selecting a road set corresponding to the road according to the road of the bus and the received bus positioning data; positioning a road where a vehicle is located according to whether the enveloping rectangle of the road contains vehicle coordinates or not; positioning a road section where a vehicle is located according to whether a road section enveloping rectangle corresponding to the road contains vehicle coordinates or not;
33 Mapping the position of the bus to the central line of the road, and marking the position of the bus on the road network; according to the obtained road section, respectively calculating the foot hanging and the foot hanging distance from the bus position to the subsection between each coordinate node, taking the subsection with the foot hanging on the subsection and the subsection with the minimum foot hanging distance, and calculating the coordinate from the bus positioning data to the foot hanging on the central line of the subsection, wherein the coordinate is the position of the bus mapped on the road network; the distance from the center line of the road to the corresponding intersection or the road section between the foot hanging parts mapped on the center line of the bus stop, namely the distance from the bus to the intersection or the bus stop.
4) Predicting the time for a bus to arrive at a downstream bus stop;
According to the position of the bus on the road network obtained in the step 3), calculating the distance between the bus and the nearest two bus stops at the downstream, and predicting the time of the bus reaching the nearest two bus stops at the downstream in real time by combining the time interval and the road congestion length data through a deep neural network model, and recording as ST.
Specifically, in the step 4), the time of the bus reaching the downstream bus stop is predicted in real time through the deep neural network model, which specifically includes:
41 Programming a model training dataset: according to the bus stops along the bus route, records of historical buses arriving at each bus stop at different positions of the road network are collected, and the collected data comprise: the distance x 1 of the bus from the target point, the current period x 2, the length x 3 of a congestion road section from the bus position to the bus stop (0 when no congestion exists), and the time y taken by the bus from the current point to the bus stop; compiling an input set X= [ X 1,x2,x3 ] and a result set Y= [ Y ];
42 Splitting the collected data into a training set and a testing set, wherein the training set is used for model training, and the testing set is used for model verification; inputting the training set into a deep neural network model, training, and calculating model errors by using a test set after training;
43 Adjusting the number of neurons of two hidden layers in the deep neural network model, repeating training until the test error is less than 5%, and determining a final prediction model;
44 When new bus positioning information is received, extracting new bus distance from a bus stop, current time period and length data of a congestion road section along the way through the position of the new bus on the road network, inputting the new bus distance, the current time period and the length data of the congestion road section along the way into the final prediction model, and predicting the time of the new bus reaching the bus stop.
The deep neural network model adopted in the step 42) is a deep neural network with a 4-layer network structure, and comprises an input layer, two hidden layers and an output layer; in initial training, the input layer contains 3 neurons, and both hidden layers contain 128 neurons; the output layer contains a neuron, both the output layer and the hidden layer use ReLU as an activation function, and the output layer does not use an activation function.
5) Calculating the time interval between two adjacent buses reaching a bus stop;
Referring to fig. 2 and 3, each bus stop in the road network is traversed, two buses closest to the bus stop are searched according to a route and respectively marked as V 1、V2, wherein V 1 is closest to the bus stop, and the time interval between the arrival of a bus V 1 and a bus V 2 at the bus stop and the arrival of the previous bus at the bus stop is calculated according to the following formula;
ΔST2＝STjm-STim
Wherein i is the serial number of the bus V 1 in the bus line; j is the serial number of the bus V 2 in the bus line; k is the serial number of the bus which arrives at the bus stop before; m is the serial number of a bus stop in a bus line; f m is the estimated bus stop arrival time of the first bus; ST im is the predicted time for the bus V 1 to arrive at the bus stop; ST jm is the predicted time for the bus V 2 to arrive at the bus stop; ST km is the time when the previous bus arrives at the bus stop; delta ST 1 is the time interval from the arrival of the previous bus at the bus stop when the bus V 1 arrives at the bus stop; Δst 2 is the time interval from arrival of bus V 1 at the bus stop when bus V 2 arrives at the bus stop.
6) Setting bus priority;
The bus shift interval is I, the bus with the time difference delta ST mark is traversed, and when delta ST is more than I, the bus arrival state is marked as late arrival, namely, the late time D=delta ST-I; when delta ST is approximately equal to I, the bus arrival state is marked as quasi-point arrival; when delta ST is less than I, the bus arrival state is marked as arriving in advance; setting delay levels of buses reaching late buses to the next bus stop according to a bus delay level dividing table, and opening priority passing permission of the buses; and closing the priority passing authority of the bus when the alignment point arrives or arrives in advance.
7) Predicting the time of a bus reaching an intersection;
according to the position of the bus on the road network obtained in the step 3), searching all intersections in 1000m from the downstream of the bus along the line, calculating the distance between the bus and each intersection, and predicting the time for the bus to reach the intersection in real time through a deep neural network model according to the time interval and the road congestion length data, and marking as CT.
8) Implementing bus signal priority control;
Traversing an intersection where buses with priority control authority arrive in two signal periods, searching a pre-configured intersection signal phase adjustable amplitude table under different bus delay levels, acquiring phase adjustment constraint, and setting phase constraint according to the highest delay level when a plurality of buses exist in different delay states of the same phase; according to the predicted bus arrival intersection time CT, the signal scheme of the current intersection is combined, two-stage control is optimized in real time through the period scheme optimization and the phase, and the delay time of the bus at the intersection is reduced.
The period scheme optimization specifically comprises the following steps: when the yellow lamp of the last phase of each signal period starts, starting a scheme optimization process of the next signal period; if the predicted time when the bus arrives at the intersection is within the bus passing phase release time, maintaining the original scheme unchanged, otherwise, optimizing the scheme according to different states under the condition of phase constraint; when the bus arrives before the traffic phase is started, shortening the phase duration before the traffic phase; if the bus arrives after the passing phase is finished, the passing phase and the duration of the front phase are prolonged, and the bus is ensured to directly pass through the intersection without stopping.
The phase real-time optimization specifically comprises the following steps: in the process of periodically executing signals at the intersection, dynamically monitoring the time when the bus arrives at the intersection in the period, and dynamically prolonging or shortening the phase duration under the condition of phase constraint when the bus arrives at the time and the arrival time predicted in the period optimization process deviate, so that the probability that the bus does not stop passing through the intersection is increased; when the bus fails to pass in the period, the time length of the remaining phase in the period is shortened, and the waiting time of the bus at an intersection is reduced.
The present invention has been described in terms of the preferred embodiments thereof, and it should be understood by those skilled in the art that various modifications can be made without departing from the principles of the invention, and such modifications should also be considered as being within the scope of the invention.
Claims (6)
1. A bus signal priority control method for realizing balanced distribution of buses is characterized by comprising the following steps:
1) Dividing delay grades of buses reaching bus stops;
2) Setting the maximum adjustable amplitude of the intersection signal phase according to the delay level of the bus;
3) Mapping the position of the bus on the road network, and calculating the distance from the bus to the downstream intersection and the bus stop;
4) Predicting the time for a bus to arrive at a downstream bus stop;
5) Calculating the time interval of two adjacent buses in the same line reaching a bus stop;
6) Analyzing whether the bus is delayed or not and the delay level, and setting the bus priority;
7) Predicting the time of a bus reaching an intersection;
8) Implementing bus signal priority control;
The step 3) specifically comprises the following steps: the method comprises the steps of collecting bus coordinates, speed and heading information in real time through vehicle-mounted positioning equipment, and calculating the position of a bus on a road network through a GIS space analysis method by combining high-precision road network data, bus route paths and bus stop information, wherein the steps include mapping the position of the bus on a road center line and calculating the distance from the vehicle to the end point of a road section;
The GIS space analysis method is used for calculating the position of the bus on the road network and specifically comprises the following steps:
31 Constructing a road and road section two-stage data set according to the high-precision road network data and the bus line; extracting road data passing through a bus route, calculating an envelope rectangle of each road data, and constructing a road set containing road codes and road envelope rectangle fields; respectively constructing a road section set comprising road codes, road section codes and road section enveloping rectangles aiming at road sections contained by each road; the envelope rectangle is a boundary rectangle formed by minimum longitude and latitude coordinates and maximum longitude and latitude coordinates in boundary nodes of a road or a road section;
32 Positioning the road section of the bus, and selecting a road set corresponding to the road according to the road of the bus and the received bus positioning data; positioning a road where a vehicle is located according to whether the enveloping rectangle of the road contains vehicle coordinates or not; positioning a road section where a vehicle is located according to whether a road section enveloping rectangle corresponding to the road contains vehicle coordinates or not;
33 Mapping the position of the bus to the central line of the road, and marking the position of the bus on the road network; according to the obtained road section, respectively calculating the foot hanging and the foot hanging distance from the bus position to the subsection between each coordinate node, taking the subsection with the foot hanging on the subsection and the subsection with the minimum foot hanging distance, and calculating the coordinate from the bus positioning data to the foot hanging on the central line of the subsection, wherein the coordinate is the position of the bus mapped on the road network; the distance from the center line of the foot to the corresponding intersection or the road section between foot sections mapped on the center line of the bus stop, namely the distance from the bus to the intersection or the bus stop;
the step 4) specifically comprises the following steps: calculating to obtain the distance between the bus and the nearest two bus stops on the downstream according to the position of the bus on the road network obtained in the step 3), and predicting the time of the bus reaching the nearest two bus stops on the downstream in real time by combining the time interval and road congestion length data through a deep neural network model, and marking as ST;
the step 8) specifically includes:
Traversing an intersection where buses with priority control authority arrive in two signal periods, searching a pre-configured intersection signal phase adjustable amplitude table under different bus delay levels, acquiring phase adjustment constraint, and setting phase constraint according to the highest delay level when a plurality of buses exist in different delay states of the same phase; according to the predicted bus arrival intersection time CT, combining with the signal scheme of the current intersection, optimizing two-stage control in real time through the period scheme and the phase, and reducing the delay time of the bus at the intersection;
The period scheme optimization specifically comprises the following steps: when the yellow lamp of the last phase of each signal period starts, starting a scheme optimization process of the next signal period; if the predicted time when the bus arrives at the intersection is within the bus passing phase release time, maintaining the original scheme unchanged, otherwise, optimizing the scheme according to different states under the condition of phase constraint; when the bus arrives before the traffic phase is started, shortening the phase duration before the traffic phase; if the bus arrives after the passing phase is finished, the passing phase and the duration of the front phase are prolonged, and the bus is ensured to directly pass through the intersection without stopping;
The real-time optimization of the phase specifically comprises the following steps: in the process of periodically executing signals at the intersection, dynamically monitoring the time when the bus arrives at the intersection in the period, and dynamically prolonging or shortening the phase duration under the condition of phase constraint when the bus arrives at the time and the arrival time predicted in the period optimization process deviate, so that the probability that the bus does not stop passing through the intersection is increased; when the bus fails to pass in the period, the time length of the remaining phase in the period is shortened, and the waiting time of the bus at an intersection is reduced.
2. The bus signal priority control method for realizing balanced bus distribution according to claim 1, wherein the step 1) specifically comprises: the delay state is divided into slight delay, general delay and serious delay according to the delay time of predicting the arrival of the bus at the station; and the delay state is that the time interval of two adjacent buses on the same line reaching the same station is calculated, if the time interval exceeds the shift interval in the shift schedule, the delay of the next bus is judged, and the delay grade of the next bus is divided according to the delay grade dividing table.
3. The bus signal priority control method for realizing balanced bus distribution according to claim 1, wherein in the step 4), the time of the bus reaching the downstream bus stop is predicted in real time through a deep neural network model, specifically comprising:
41 Programming a model training dataset: according to the bus stops along the bus route, records of historical buses arriving at each bus stop at different positions of the road network are collected, and the collected data comprise: the distance x 1 of the bus from the target point, the current period x 2, the length x 3 of a congestion road section from the position of the bus to the bus stop along the way, and the time y taken by the bus from the current point to the bus stop; compiling an input set X= [ X 1,x2,x3 ] and a result set Y= [ Y ];
42 Splitting the collected data into a training set and a testing set, wherein the training set is used for model training, and the testing set is used for model verification; inputting the training set into a deep neural network model, training, and calculating model errors by using a test set after training;
43 Adjusting the number of neurons of two hidden layers in the deep neural network model, repeating training until the test error is less than 5%, and determining a final prediction model;
44 When new bus positioning information is received, extracting new bus distance from a bus stop, current time period and length data of a congestion road section along the way through the position of the new bus on the road network, inputting the new bus distance, the current time period and the length data of the congestion road section along the way into the final prediction model, and predicting the time of the new bus reaching the bus stop.
4. The bus signal priority control method for realizing balanced bus distribution according to claim 1, wherein the step 5) specifically comprises:
Traversing each bus stop in the road network, searching two buses closest to the bus stop according to a route, respectively marking as V 1、V2, wherein V 1 is closest to the bus stop, and respectively calculating the time interval between the arrival of a bus V 1 and a bus V 2 at the bus stop and the arrival of the last bus at the bus stop according to the following formula;
ΔST2＝STjm-STim
Wherein i is the serial number of the bus V 1 in the bus line; j is the serial number of the bus V 2 in the bus line; k is the serial number of the bus which arrives at the bus stop before; m is the serial number of a bus stop in a bus line; f m is the estimated bus stop arrival time of the first bus; ST im is the predicted time for the bus V 1 to arrive at the bus stop; ST jm is the predicted time for the bus V 2 to arrive at the bus stop; ST km is the time when the previous bus arrives at the bus stop; delta ST 1 is the time interval from the arrival of the previous bus at the bus stop when the bus V 1 arrives at the bus stop; Δst 2 is the time interval from arrival of bus V 1 at the bus stop when bus V 2 arrives at the bus stop.
5. The bus signal priority control method for realizing balanced bus distribution according to claim 1, wherein the step 6) specifically comprises:
The bus shift interval is I, the bus with the time difference delta ST mark is traversed, and when delta ST > I, the bus arrival state is marked as late arrival, namely, the late time D=delta ST-I; when delta ST is approximately equal to I, the bus arrival state is marked as quasi-point arrival; when delta ST < I, the bus arrival state is marked as arriving in advance; setting delay levels of buses reaching late buses to the next bus stop according to a bus delay level dividing table, and opening priority passing permission of the buses; and closing the priority passing authority of the bus when the alignment point arrives or arrives in advance.
6. The bus signal priority control method for realizing balanced bus distribution according to claim 1, wherein the step 7) specifically comprises:
according to the position of the bus on the road network obtained in the step 3), searching all intersections in 1000m from the downstream of the bus along the line, calculating the distance between the bus and each intersection, and predicting the time for the bus to reach the intersection in real time through a deep neural network model according to the time interval and the road congestion length data, and marking as CT.
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