CN112365714A - Traffic signal control method for intersection of intelligent rail passing main branch road - Google Patents
Traffic signal control method for intersection of intelligent rail passing main branch road Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G08G—TRAFFIC CONTROL SYSTEMS
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- G08G—TRAFFIC CONTROL SYSTEMS
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- G08G1/07—Controlling traffic signals
- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/095—Traffic lights
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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Abstract
The invention provides a traffic signal control method for a main branch intersection for intelligent rail transit, which takes the flow ratio of a branch to a main trunk and the total flow and saturated flow ratio of the intersection as input, calculates an initial signal period through a fuzzy controller, and calculates a complete signal timing scheme through a Webster timing method; by arranging the special indicator lamp for borrowing the road, when an intelligent rail runs, the signal timing of the intersection is adjusted in an induction mode; when the intelligent rail does not run, other vehicles are allowed to run by the way. Simulation test results show that compared with fixed timing and simple fuzzy control, the intelligent rail control method is better suitable for signal control when the intelligent rail passes through the main trunk-branch intersection; the invention improves the evaluation indexes of vehicle queuing length, average delay, parking times and the like, improves the carrying capacity of urban public transport and the control efficiency of the mixed flow of vehicles with intelligent rails, and provides a research basis for the main road-branch road intersection for the automatic driving and manual driving of the mixed flow of vehicles to pass.
Description
Technical Field
The invention belongs to the technical field of traffic signal timing, and particularly relates to a traffic signal control method for an intersection of an intelligent rail transit main branch road.
Background
With the explosive development of the automatic driving technology, more and more automatic driving related technologies are applied to practice. An intelligent rail express delivery system (automated rail Transit-ART) is also a newly emerging automatic driving vehicle, and when more and more intelligent rails are put into operation, the efficiency of signal control of intersections where the intelligent rails pass through is more and more important.
Many scholars have begun to study the operation of smart rails, but there has been less research on smart rail signal control. Particularly, when the intelligent rail train completely enters the city, after the intelligent rail special lane is laid and operated and used, signal timing is one of the key factors for ensuring the operation efficiency of the intersection. At present, signal timing of intersections is mainly divided into three types:
1) when fixed timing, planning signal phases according to historical data of intersections and flow ratios of inlets and calculating green light display time of each phase;
2) induction control, wherein a vehicle detector is arranged to detect vehicles arriving at the upstream, and the green time of a certain phase is increased to meet the traffic demand;
3) self-adaptive control, under the condition of no human intervention, the self-control parameters are automatically adjusted along with the change of the operating environment, so as to achieve the optimal control.
Due to the particularity of the intelligent rail, the intelligent rail does not decelerate and directly passes through the intersection of the main road and the branch road. Therefore, the whole operation efficiency of the intersection is ensured while the intelligent rail passing is met.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the traffic signal control method for the intelligent rail passing main trunk road intersection is used for improving the efficiency of the intelligent rail passing the main trunk road-branch road intersection.
The technical scheme adopted by the invention for solving the technical problems is as follows: a traffic signal control method for a main branch intersection for intelligent rail transit comprises the following steps:
s1: adding an intelligent rail special lane on a road section where the intersection is located and normally enabling the vehicle to pass;
s2: detecting the traffic flow of the intersection through a ground induction coil;
s3: calculating the ratio of the incoming traffic flow to the saturated flow and the ratio of the branch flow to the main trunk flow, and taking the ratios as input variables of the fuzzy controller;
s4: formulating a fuzzy rule according to actual conditions, and taking an initial signal period as an output variable;
s5: calculating a signal period complete scheme according to the initial signal period by adopting a Webster timing method;
s6: the method is characterized in that a special track of an intelligent rail (ART) is controlled, the intelligent rail is ensured to pass through an intersection without stopping as much as possible, and vehicles are allowed to pass through the track when no intelligent rail passes through the road section.
According to the scheme, in the step S4, the specific steps are as follows:
s41: describing the ratio of the branch road traffic flow to the main road traffic flow by adopting a grade unit;
s42: describing the ratio of the vehicle flow to the saturated flow by adopting a grade unit;
s43: fuzzifying the initial period duration;
s44: the ratio of the incoming traffic flow to the saturated flow and the ratio of the branch flow to the main line flow are input to the fuzzy controller, and an initial signal period is output.
Further, in step S41, the specific steps include: and recording the ratio of the branch road traffic flow to the main road traffic flow as 5 fuzzy sets of B (large), PB (large), M (medium), PS (small) and S (small), wherein the membership function adopts a trimf function.
Further, in step S42, the specific steps include: and recording the ratio of the traffic flow to the saturated flow as 5 fuzzy sets of B (large), PB (large), M (medium), PS (small) and S (small), wherein the membership function adopts a trimf function.
Further, in step S43, the specific steps include: adopting L (low), M (middle) and H (high) 3 fuzzy sets to represent the time length of an initial period; the fuzzy rule adopts the rule of 'IF A THEN B'.
According to the scheme, in the step S6, the specific steps are as follows:
s61: setting a special intelligent rail special lane signal lamp;
s62: pre-judging whether an intelligent rail passes in the next signal period duration according to the intelligent rail departure time and the position, and if so, not allowing other motor vehicles to borrow an intelligent rail special lane; if not, allowing other motor vehicles to borrow the intelligent rail special lane;
s63: and when the intelligent rail enters the intersection range, judging the lighting logic of the traffic signal lamp.
Further, in step S63, the specific steps include:
s631: let L be the length of the intelligent rail train L1For the distance from the rail head to the crossing, l2The width of the approach way of the West import and the travel speed of the intelligent rail v are defined as the time when the intelligent rail enters the range of the intersection
Time for intelligent rail to reach intersection without deceleration
S632: if the main road is in the green light state, judging the residual green light time tgWhether it is enough to pass through the intersection without stopping the intelligent rail: when the remaining green light time tg＞t1When the intelligent rail passes through the intersection without stopping, the signal timing scheme of the intersection is not changed; when t isg＜t1In the meantime, let the extension time of the green light time of the trunk road be t1-tg；
S633: if the main road is red light, judging the remaining red lightTime trWhether the intelligent rail can be forced to stop for waiting: when t isr＜t2When the signal timing scheme is not changed; when t isr＞t2When the red light is finished in advance, the advance time length is tr-t2。
A computer storage medium, characterized in that: the intelligent rail transit main branch intersection traffic signal control method comprises a step of storing a computer program which can be executed by a computer processor, and a step of executing the intelligent rail transit main branch intersection traffic signal control method.
The invention has the beneficial effects that:
1. the intelligent rail passing main branch intersection traffic signal control method performs signal control on a main branch-branch intersection through fuzzy logic, considers the traffic flow change of the main branch and the ratio of the total traffic flow to the saturated traffic flow, and performs dynamic timing control on the main branch and the branch intersection; the simulation test result shows that compared with fixed timing and simple fuzzy control, the intelligent rail control method is better suitable for signal control when the intelligent rail passes through the main trunk-branch intersection, and the function of improving the efficiency when the intelligent rail passes through the main trunk-branch intersection is realized.
2. The invention improves the evaluation indexes of vehicle queuing length, average delay, parking times and the like, and provides a research basis for the main road-branch intersection where the automatic driving and manual driving mixed traffic flows pass.
3. The intelligent-rail-borrowing-dedicated lane is provided with the lane-borrowing signal, other vehicles are allowed to borrow the lane to run when no intelligent rail passes, and the idle waste of the dedicated lane is avoided.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a graph of membership function of branch to trunk flow ratio according to an embodiment of the present invention.
FIG. 3 is a graph of membership function for incoming traffic saturation in accordance with an embodiment of the present invention.
FIG. 4 is a graph of output membership functions for an embodiment of the present invention.
FIG. 5 is a fuzzy aggregation map of an embodiment of the present invention.
Fig. 6 is an intersection diagram of the embodiment of the present invention.
FIG. 7 is a signal phase diagram of an embodiment of the present invention.
Fig. 8 is a simulated poisson process diagram for each incoming vehicle per second for each entrance in accordance with an embodiment of the present invention.
FIG. 9 is a graph of signal period variations for an embodiment of the present invention.
Fig. 10 is a graph comparing signal periods during peak and off-peak periods for an embodiment of the invention.
FIG. 11 is a diagram of simulation results for an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fuzzy control is an artificial intelligence algorithm, and is characterized in that the human thinking is simulated, the quantitative expression is not carried out by specific values, but the physical phenomenon is described by fuzzy human languages such as a plurality of, more and medium languages. The invention adopts fuzzy logic to carry out signal control on the intersection of the main road and the branch road, and takes the traffic flow change of the main road and the branch road and the ratio of the total flow and the saturated flow of the intersection into consideration to carry out dynamic timing control on the intersection of the main road and the branch road; meanwhile, setting a lane borrowing signal of the intelligent rail special lane, and allowing other vehicles to borrow the lane to run when no intelligent rail passes so as to avoid idle and waste of the special lane; and finally, verifying the optimization effect by adopting Python and VISSIM combined simulation.
Referring to fig. 1, the method for controlling traffic signals at the intersection of the intelligent rail transit main branch comprises the following steps:
s1: adding an intelligent rail special lane on a road section where the intersection is located and normally enabling the vehicle to pass;
s2: detecting the traffic flow of the intersection through a ground induction coil;
s3: calculating the ratio of the incoming traffic flow to the saturated flow and the ratio of the branch flow to the main trunk flow, and taking the ratios as input variables of the fuzzy controller;
s4: a fuzzy rule is formulated according to actual conditions, and an initial signal period is taken as an output variable:
s41: describing the ratio of branch road traffic flow to main road traffic flow by using grade units, and recording as 5 fuzzy sets of B (large), PB (large), M (medium), PS (small) and S (small); referring to fig. 2, the membership function is a trimf function, and the selection of the membership function can be adjusted according to actual conditions; the intersection related to the embodiment of the invention is a typical main intersection, and in the signal timing calculation, the ratio of the branch road traffic flow to the main road traffic flow is divided into [0, 0.5 ];
s42: describing the ratio of the traffic flow to the saturated flow by adopting a grade unit, and recording as 5 fuzzy sets of B (large), PB (large), M (medium), PS (small) and S (small); referring to fig. 3, the membership function is a trimf function; the embodiment of the invention divides the ratio of the traffic flow and the saturated flow into [0.1, 1 ];
s43: fuzzification initial period duration is expressed by adopting L (low), M (medium) and H (high) 3 fuzzy sets; the fuzzy rule adopts the rule of 'IF A THEN B', and the total number of the fuzzy rules is 25. The fuzzy rule is specifically formulated as follows:
TABLE 1 fuzzy rules
Referring to fig. 3, the output membership function is a trimf function; the fuzzy set is shown in fig. 4, and the fuzzy controller judges the appropriate initial period duration according to the total flow at the intersection and the ratio of the branch road traffic flow to the main road traffic flow. For example, when the ratio of branch to trunk traffic is large, there are two possibilities: firstly, the total traffic flow at the intersection is not large, the branch traffic flow is sparse, and only a small initial signal period needs to be given at the moment; second, the total traffic flow at the intersection is high and the branch traffic flow is near saturation, at which time a large initial signal period must be used. The initial period range for an embodiment of the present invention is [80, 120 ].
S5: after the initial signal period is obtained, calculating a complete scheme of the signal period by a Webster timing method;
s6: the method is characterized in that a special track of an intelligent rail (ART) is controlled, the intelligent rail is ensured to pass through an intersection without stopping as much as possible, and vehicles are allowed to pass through the track when no intelligent rail passes through the road section.
The intelligent rail needs a special lane in the driving process, but the special channel of the intelligent rail has no structural difference with other motor vehicle lanes, and the difference is only reflected on a road marking line; the invention allows the motor vehicle to borrow the intelligent rail special lane on the premise of ensuring the operation safety of the intelligent rail through the control signal.
S61: a special intelligent rail special lane signal lamp is arranged, and can be in a text form or other types;
s62: pre-judging whether an intelligent rail passes in the next signal period duration according to the intelligent rail departure time and the position, and if so, not allowing other motor vehicles to borrow an intelligent rail special lane; if not, allowing other motor vehicles to borrow the intelligent rail special lane;
s63: let L be the length of the intelligent rail train L1For the distance from the rail head to the crossing, l2The width of approach way of West import and v the travel speed of the intelligent rail, when the intelligent rail enters the position of figure 6, the time of the intelligent rail passing through the intersection is
Time for intelligent rail to reach intersection without deceleration
If the main road is in the green light state, judging the residual green light time tgWhether it is enough to pass through the intersection without stopping the intelligent track: when the remaining green light time tg＞t1When the intelligent rail passes through the intersection without stopping, the signal timing scheme of the intersection is not changed; when t isg＜t1In the meantime, let the extension time of the green light time of the trunk road be t1-tg；
If the main road is red, judging the residual red time trWhether the intelligent rail can be forced to stop for waiting: when t isr＜t2When the signal timing scheme is not changed; when t isr＞t2While, redThe lamp is ended in advance, the time length in advance is tr-t2。
The simulation data of the embodiment of the invention is based on the X street-C street intersection of H city, and the equivalent traffic volume after conversion is shown in the table 2. The current situation is shown in fig. 6, the north-south direction is a main road, the east-west direction is a branch road, the west-side outlet is a one-way road, the east-side inlet is a two-way lane, the north-south inlet is a two-way four-way lane, the specific traffic data is shown in table 3, and the existing signal timing phase is shown in fig. 7. And calculating the saturated flow according to the traffic survey data, wherein the saturated flow of the east inlet is 1586veh/h, the saturated flow of the north inlet is 3032veh/h, and the saturated flow of the south inlet is 2655 veh/h.
TABLE 2 equivalent traffic table after conversion
TABLE 3 traffic data sheet
| An inlet | East | South China | North China |
| Road grade | Branch | Arterial | Arterial |
| Design speed | 40km/h | 60km/h | 60km/h |
| Road width | 6.75m | 16m | 16m |
| Number of |
1 | 4 | 4 |
| Lane width | 3.75m | 3.75m | 3.75m |
| Approach length | 111m | 96m | 80m |
The embodiment of the invention adds the intelligent rail special lane at the outer side of the main road, and the lane width is 3.75 m. And is distinguished from a common motor vehicle lane by a yellow solid line. If the intelligent rail normally operates, 3 sections of marshalling are adopted for 32m, and the inter-row spacing is the same as that of a subway in H city: the inter-vehicle interval in the peak period is 4 minutes and 51 seconds, the inter-vehicle interval in the flat period is 6 minutes and 30 seconds, and the inter-vehicle interval in the low period is 10 minutes; and simulating for 11.5h according to the running time. In order to make the comparison result more obvious, the original signal period and phase are not changed, see fig. 7; and the period of the first run is calculated according to the existing period (82 s).
The simulation of the embodiment of the invention adopts the combined simulation of Python and VISSIM, and the simulation steps are as follows:
the method comprises the following steps: according to survey data, simulating the poisson process of vehicles coming from each inlet every second by utilizing Python, and referring to FIG. 8;
step two: calculating a signal timing scheme of the intersection;
step three: setting a control scheme of the intelligent rail;
step four: compared with the operation effect of three control methods of fuzzy control and fuzzy control (borrowing) when fixed timing is adopted.
The cycle duration obtained by the fuzzy control is shown in fig. 9 and fig. 10, it can be seen that the intelligent rail driving interval is smaller in the peak period (4 th hour), so that the jump change of the signal cycle is more intensive; and in off-peak time, the intelligent rail vehicle interval is increased, so that the jump change of the signal period is sparse. The off-peak period duration is generally higher than the peak period because the traffic flow on the main road and the branch road, particularly the main road, is very high during the peak period. If the signal period is set to be a large value, the green light duration of the main line is far longer than that of the branch line in the timing scheme obtained according to the Webster timing method. Such a long period can greatly reduce the traffic efficiency of the branch road, so that the period length needs to be set properly to ensure the overall operation efficiency of the intersection, which is one of the advantages of the fuzzy control in the signal timing application provided by the invention.
Referring to fig. 11, the vehicle queue length under fixed timing and fuzzy control behaves approximately the same, while under fuzzy control (lane borrowing) the average vehicle queue length drops significantly, by 7.7m (28.5%) and 8.0m (29.3%), respectively. Wherein, for t-tests of fuzzy control and fuzzy control (borrowing), P is less than 0.001.
The average number of stops for fuzzy control is relatively small compared to the fixed timing scheme (72veh, 25.7%). Under fuzzy control (lane borrowing), the descending trend is more obvious, and the average parking times are reduced by 34veh (16.3%) compared with the scheme without lane borrowing, and P in t test is less than 0.001.
The two fuzzy control schemes have the most remarkable optimization effect on delay, and the average delay of fuzzy control (borrowing) is reduced by 31.91s (70.8%) compared with fixed timing. P <0.001 in T-test. Moreover, the optimization effect of fuzzy control (borrowing) is more obvious in the peak period, and the queuing length is respectively reduced by 17.4m (37.5%) and 13.3m (31.4%) compared with fuzzy control and fixed distribution; the average number of stops decreased by 78veh (27.1%) and 164veh (43.8%), respectively; the average delay decreased by 4.4s (22.0%) and 39.1s (71.5%), respectively.
TABLE 4 simulation results
The invention provides a control method for an intersection of a main road and a branch road, which aims to enable an intelligent rail to pass through the intersection of the main road and the branch road more efficiently and ensure the traffic capacity of the intersection when no intelligent rail passes through the intersection. The method takes the ratio of the branch road traffic flow to the main road traffic flow and the ratio of the intersection total traffic flow to the saturated flow as input variables, obtains an initial period through a fuzzy controller, and obtains a signal timing scheme through calculation of a Webster timing method. When the intelligent rail passes, the signal timing is required to be adjusted according to the actual situation so as to ensure that the intelligent rail can pass through the intersection without deceleration; when no intelligent rail passes, other motor vehicles are allowed to borrow the intelligent rail special lane.
The invention simulates the running condition of the intersection through Python, calculates a signal timing scheme and simulation parameters, and then carries out the running simulation of the intersection through the VISSIM. Compared with fixed timing and fuzzy control, the fuzzy control (lane-borrowing) scheme provided by the invention has obvious optimization effect on vehicle queuing, parking times, average delay and the like. The research result of the invention provides a research basis for the main road-branch road intersection for the mixed traffic flow of automatic driving and manual driving.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (8)
1. A traffic signal control method for a main branch intersection for intelligent rail transit is characterized by comprising the following steps: the method comprises the following steps:
s1: adding an intelligent rail special lane on a road section where the intersection is located and normally enabling the vehicle to pass;
s2: detecting the traffic flow of the intersection through a ground induction coil;
s3: calculating the ratio of the incoming traffic flow to the saturated flow and the ratio of the branch flow to the main road flow, and taking the ratios as input variables of the fuzzy controller;
s4: formulating a fuzzy rule according to actual conditions, and taking an initial signal period as an output variable;
s5: calculating a signal period complete scheme according to the initial signal period by adopting a Webster timing method;
s6: the method is characterized in that a special track of an intelligent rail (ART) is controlled, the intelligent rail is ensured to pass through an intersection without stopping as much as possible, and vehicles are allowed to pass through the track when no intelligent rail passes through the road section.
2. The traffic signal control method for the intelligent rail transit main branch road intersection according to claim 1, characterized in that: in the step S4, the specific steps are as follows:
s41: describing the ratio of the branch road traffic flow to the main road traffic flow by adopting a grade unit;
s42: describing the ratio of the vehicle flow to the saturated flow by adopting a grade unit;
s43: fuzzifying the initial period duration;
s44: the ratio of the incoming traffic flow to the saturated flow and the ratio of the branch flow to the main line flow are input to the fuzzy controller, and an initial signal period is output.
3. The traffic signal control method for the intelligent rail transit main branch road intersection according to claim 2, characterized in that: in the step S41, the specific steps are as follows: and recording the ratio of the branch road traffic flow to the main road traffic flow as 5 fuzzy sets of B (large), PB (large), M (medium), PS (small) and S (small), wherein the membership function adopts a trimf function.
4. The traffic signal control method for the intelligent rail transit main branch road intersection according to claim 2, characterized in that: in the step S42, the specific steps are as follows: and recording the ratio of the traffic flow to the saturated flow as 5 fuzzy sets of B (large), PB (large), M (medium), PS (small) and S (small), wherein the membership function adopts a trimf function.
5. The traffic signal control method for the intelligent rail transit main branch road intersection according to claim 2, characterized in that: in the step S43, the specific steps are as follows: adopting L (low), M (middle) and H (high) 3 fuzzy sets to represent the time length of an initial period; the fuzzy rule adopts the rule of 'IF A THEN B'.
6. The traffic signal control method for the intelligent rail transit main branch road intersection according to claim 1, characterized in that: in the step S6, the specific steps are as follows:
s61: setting a special intelligent rail special lane signal lamp;
s62: pre-judging whether an intelligent rail passes in the next signal period duration according to the intelligent rail departure time and the position, and if so, not allowing other motor vehicles to borrow an intelligent rail special lane; if not, allowing other motor vehicles to borrow the intelligent rail special lane;
s63: and when the intelligent rail enters the intersection range, judging the lighting logic of the traffic signal lamp.
7. The method for controlling traffic signals at the intersection of the intelligent rail transit main branch road according to claim 6, wherein: in the step S63, the specific steps are as follows:
s631: let L be the length of the intelligent rail train L1For the distance from the rail head to the crossing, l2Width of approach way at west entrance, v is intelligent rail travel speedWhen the intelligent rail enters the range of the intersection, the time for the intelligent rail to pass through the intersection is
Time for intelligent rail to reach intersection without deceleration
S632: if the main road is in the green light state, judging the residual green light time tgWhether it is enough to pass through the intersection without stopping the intelligent rail: when the remaining green light time tg＞t1When the intelligent rail passes through the intersection without stopping, the signal timing scheme of the intersection is not changed; when t isg＜t1In the meantime, let the extension time of the green light time of the trunk road be t1-tg；
S633: if the main road is red, judging the residual red time trWhether the intelligent rail can be forced to stop for waiting: when t isr＜t2When the signal timing scheme is not changed; when t isr＞t2When the red light is finished in advance, the advance time length is tr-t2。
8. A computer storage medium, characterized in that: stored therein is a computer program executable by a computer processor, the computer program executing a method for controlling traffic signals at a smart rail transit primary leg intersection according to any one of claims 1 to 7.
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| CN113870566A (en) * | 2021-11-10 | 2021-12-31 | 通号万全信号设备有限公司 | Method and device for passing through intersection without stopping train |
| CN114299730A (en) * | 2021-12-22 | 2022-04-08 | 中铁四院集团新型轨道交通设计研究有限公司 | Intelligent rail train passing control method, system, equipment and readable medium |
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101206801A (en) * | 2007-12-17 | 2008-06-25 | 青岛海信网络科技股份有限公司 | Self-adaption traffic control method |
| CN103426294A (en) * | 2012-05-15 | 2013-12-04 | 同济大学 | Multilayer fuzzy division method based on emergency traffic flow priority level |
| CN106297329A (en) * | 2016-08-26 | 2017-01-04 | 南京蓝泰交通设施有限责任公司 | A kind of signal timing dial adaptive optimization method of networking signals machine |
| CN106971566A (en) * | 2017-05-04 | 2017-07-21 | 无锡安邦电气股份有限公司 | Self-adaptation control method based on genetic algorithm |
| US20180292224A1 (en) * | 2017-04-05 | 2018-10-11 | Gregory Brodski | System and method for traffic volume estimation |
| CN110164152A (en) * | 2019-07-03 | 2019-08-23 | 西安工业大学 | One kind being used for isolated traffic intersection traffic light control system |
| CN110211396A (en) * | 2019-05-30 | 2019-09-06 | 华南理工大学 | A kind of dynamic regulation method of freeway toll station and periphery intersection group |
| CN110634293A (en) * | 2019-09-26 | 2019-12-31 | 同济大学 | Trunk intersection control method based on fuzzy control |
| WO2020071040A1 (en) * | 2018-10-05 | 2020-04-09 | 住友電工システムソリューション株式会社 | Traffic index computation device, computation method, traffic signal control system, and computer program |
| CN111243301A (en) * | 2020-01-15 | 2020-06-05 | 长沙理工大学 | Traffic signal lamp green light duration determination device, method and system |
| CN111739284A (en) * | 2020-05-06 | 2020-10-02 | 东华大学 | Traffic signal lamp intelligent timing method based on genetic algorithm optimization fuzzy control |
-
2020
- 2020-11-11 CN CN202011257046.3A patent/CN112365714B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101206801A (en) * | 2007-12-17 | 2008-06-25 | 青岛海信网络科技股份有限公司 | Self-adaption traffic control method |
| CN103426294A (en) * | 2012-05-15 | 2013-12-04 | 同济大学 | Multilayer fuzzy division method based on emergency traffic flow priority level |
| CN106297329A (en) * | 2016-08-26 | 2017-01-04 | 南京蓝泰交通设施有限责任公司 | A kind of signal timing dial adaptive optimization method of networking signals machine |
| US20180292224A1 (en) * | 2017-04-05 | 2018-10-11 | Gregory Brodski | System and method for traffic volume estimation |
| CN106971566A (en) * | 2017-05-04 | 2017-07-21 | 无锡安邦电气股份有限公司 | Self-adaptation control method based on genetic algorithm |
| WO2020071040A1 (en) * | 2018-10-05 | 2020-04-09 | 住友電工システムソリューション株式会社 | Traffic index computation device, computation method, traffic signal control system, and computer program |
| CN110211396A (en) * | 2019-05-30 | 2019-09-06 | 华南理工大学 | A kind of dynamic regulation method of freeway toll station and periphery intersection group |
| CN110164152A (en) * | 2019-07-03 | 2019-08-23 | 西安工业大学 | One kind being used for isolated traffic intersection traffic light control system |
| CN110634293A (en) * | 2019-09-26 | 2019-12-31 | 同济大学 | Trunk intersection control method based on fuzzy control |
| CN111243301A (en) * | 2020-01-15 | 2020-06-05 | 长沙理工大学 | Traffic signal lamp green light duration determination device, method and system |
| CN111739284A (en) * | 2020-05-06 | 2020-10-02 | 东华大学 | Traffic signal lamp intelligent timing method based on genetic algorithm optimization fuzzy control |
Non-Patent Citations (5)
| Title |
|---|
| 刘光勇 等: "智能轨道快运系统路权控制技术研究", 《控制与信息技术》 * |
| 巴兴强 等: "基于arduino的紧急车辆优先通行交叉口信号控制设计", 《农机使用与维修》 * |
| 王磊 等: "城市交通信号灯的配时优化", 《吉首大学学报（自然科学版）》 * |
| 谭俊桦 等: "智轨列车路口信号配时方法研究", 《计算机时代》 * |
| 陈纪龙 等: "一种用于应急情况下的交通信号灯改变装置设计", 《黑龙江交通科技》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112150804A (en) * | 2020-08-31 | 2020-12-29 | 中国地质大学（武汉） | City multi-type intersection identification method based on MaskRCNN algorithm |
| CN113409599A (en) * | 2021-06-16 | 2021-09-17 | 河南省城乡规划设计研究总院股份有限公司 | Urban public transport priority coordination control method based on information prediction |
| CN113870566A (en) * | 2021-11-10 | 2021-12-31 | 通号万全信号设备有限公司 | Method and device for passing through intersection without stopping train |
| CN114299730A (en) * | 2021-12-22 | 2022-04-08 | 中铁四院集团新型轨道交通设计研究有限公司 | Intelligent rail train passing control method, system, equipment and readable medium |
| CN114863700A (en) * | 2022-04-27 | 2022-08-05 | 中铁四院集团新型轨道交通设计研究有限公司 | Intelligent rail train operation control method, system and medium |
| CN114863700B (en) * | 2022-04-27 | 2023-02-28 | 中铁四院集团新型轨道交通设计研究有限公司 | Intelligent rail train operation control method, system and medium |
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