224 results about "Signal timing" patented technology

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.

The invention discloses a traffic drainage signal management method and system based on a Beidou system, and relates to the technical field of Beidou satellite navigation, and the method comprises the steps: obtaining the real-time traffic data of the position and speed of a vehicle at each intersection based on the Beidou system, and transmitting the real-time traffic data to a traffic management platform; carrying out feature matching on the accident identification model trained based on historical emergency data and a deep learning algorithm and real-time traffic data, and identifying an emergency; and constructing a real-time traffic situation map in combination with the identified emergency information and the traffic condition information of the intersections. The vehicle position and speed data are obtained in real time through the Beidou system, emergencies are rapidly recognized and signal lamp timing is dynamically adjusted in combination with a deep learning algorithm, and compared with a traditional fixed timing scheme, traffic flow changes can be responded in real time, frequent start and stop of vehicles are reduced, the road passing efficiency is improved, and the traffic safety is improved. And particularly, in a sudden traffic accident or construction scene, a signal strategy can be quickly optimized, and traffic congestion aggravation is avoided.

The invention relates to the technical field of motion control, in particular to a stepping motor monitoring control method and system, and the method comprises the following steps: obtaining a target and feedback signal rising edge sequence, analyzing a periodic change direction, judging a time migration trend, extracting a voltage and current response section, recognizing a disturbance time slice, and positioning high and low level boundaries. And screening signals with consistent frequency, extracting a running rhythm time sequence, and comparing a rhythm sequence to obtain a monitoring control behavior. According to the method, the time sequence of rising edges of a target and a feedback signal is compared, the displacement deviation trend is judged in combination with the periodic change direction, an interference section is identified by utilizing voltage abrupt change and current fluctuation characteristics, a control boundary is delimited according to the distribution of the interference section in high and low levels, and signal segments with consistent frequency characteristics are extracted to construct an operation rhythm. The driving behavior is extracted in combination with the signal time sequence and the rhythm sequence, the responsiveness to rhythm change is enhanced, and the problems of interference fluctuation and control rhythm disjunction caused by rhythm dislocation are relieved.

The invention relates to an automatic parallel traffic simulation analysis method and device. The method comprises the following steps: in response to a received region delimiting instruction, generating an initial digital road network from a map data source, and executing topology calibration and connectivity repair operation on the initial digital road network to generate a digital road network file; generating structured traffic demand data based on the input of the user; generating a basic traffic scene file based on the digital road network file and the traffic demand data, and forming a traffic simulation operation scene based on the configuration of the basic traffic scene file by a user; generating a plurality of signal timing schemes based on the traffic simulation operation scene; and executing traffic simulation based on the plurality of signal timing schemes, analyzing a simulation result to obtain a multi-level key performance index, performing visual comparison on the multi-level key performance index, and generating a natural language evaluation report by using a large language model. Therefore, simulation efficiency, usability and scene construction flexibility can be remarkably improved.

A system includes a processor that is configured to acquire traffic video, analyze the acquired traffic video to determine traffic conditions, and optimize traffic signal timing based on the traffic conditions, and apply the optimized signal timing to a traffic signal.

The invention provides a ramp traffic signal adaptive control method and system based on unmanned aerial vehicle cooperation, and belongs to the technical field of intelligent traffic control, and the method comprises the steps: collecting traffic data in real time based on an unmanned aerial vehicle cluster dynamically deployed over a ramp, constructing a global traffic state matrix, and carrying out the initial processing through edge calculation; carrying out edge calculation on the primarily processed data by utilizing an edge node to generate a local optimal signal timing suggestion in real time; the local optimal signal timing suggestions uploaded by the multiple unmanned aerial vehicles are integrated at the cloud, and a global optimal signal timing strategy is generated through unified training; signal lamp parameters are adjusted in real time on the basis of a global optimal signal timing strategy, the direct intervention capability on site traffic is enhanced through an air guiding function, and a high-reliability solution is provided for an intelligent traffic system.

The invention relates to the technical field of vehicles, in particular to a collaborative decision-making system and method under mixed traffic flow, a vehicle and a storage medium, and the method comprises the steps: obtaining the traffic environment information under the mixed traffic flow in a target range through a roadside sensing unit, and calculating the behavior intention probability of a manual driving vehicle through a preset intention prediction model; a vehicle end decision-making unit is utilized to construct, adjust and expand a perception domain based on traffic environment information and behavior intention probability in combination with vehicle-mounted sensor data, generate a risk constraint trajectory and a collaborative decision of the vehicle, and re-plan a driving path of the vehicle; and the cloud optimization unit is used for updating a preset intention prediction model, receiving traffic environment information, reconstructing an area-level traffic situation under mixed traffic flow, and transmitting optimized signal timing and formation parameters to the road side sensing unit and the vehicle end decision-making unit. Therefore, the problems of single adaptability of a traffic collaborative decision-making scene, no fusion of real-time road side data and the like in related technologies are solved.

The invention discloses a traffic signal control method and system based on multi-source event and double-loop phase cooperation, and relates to the technical field of traffic signal control, a road intersection mathematical model is constructed, a double-loop phase structure is set, and multi-source sensing data is collected in real time; based on the multi-source sensing data, modeling an intersection traffic signal control problem by adopting a deep Q network model, defining a state space, an action space and a reward function, decomposing a double-ring phase structure, and performing cooperative control by a plurality of agents; the reward function is constructed based on a vehicle delay time difference, a waiting time difference, a parking frequency difference and a queuing length difference; the intelligent agents are controlled to execute actions based on rewards fed back to the intelligent agents by the environment, phase keeping or phase switching is determined, an optimal phase scheme of signal control is obtained, timestamp driving, multi-event coupling analysis and a double-ring phase structure are fused, and intersection signal timing optimization is achieved in combination with a deep reinforcement learning algorithm.

The invention provides a multi-scene-oriented intelligent rail signal timing optimization method and device, and relates to the technical field of intelligent public transportation, and the method comprises the steps: building a basic signal model according to the total number of current arriving vehicles and a signal constraint condition, and carrying out the calculation of each lane operation scene, and obtaining the optimal period duration; calculating the priority buffer time according to the parameters of the lane operation scene and the geometric parameters of the intelligent rail, and constructing the unified period duration through the priority buffer time and the optimal period duration; inputting the unified period duration into the basic signal optimization model, and respectively solving each lane operation scene to generate a signal timing scheme; optimizing signal timing in the remaining time of the current period based on the signal timing scheme to obtain a transition scheme; and performing signal timing switching on the signal timing scheme of the current scene according to the transition scheme to obtain a signal timing result. According to the invention, the problem that the signal timing scheme cannot be smoothly switched in a complex traffic scene is solved.

The invention discloses an intersection signal control method based on minimum blocking probability and highest traffic capacity, and relates to the technical field of intelligent traffic systems. A bilevel planning optimization model is constructed, an upper layer model takes minimization of intersection blocking probability as a core target, a blocking probability function is established through dynamic quantification of traffic flow random volatility, and a sequential quadratic programming algorithm is fused to optimize a signal period and effective green light time parameters of each phase; the lower layer model is based on a Webster delay model, and feeds back the traffic state in real time by taking the minimization of the total delay of the vehicle as a target. The upper layer model and the lower layer model realize collaborative optimization through a closed loop mechanism of parameter adjustment-delay feedback-iterative optimization, and meanwhile, a dynamic weight coefficient is introduced to flexibly coordinate multi-target conflicts of the blocking probability, delay and traffic capacity. According to the method, the dynamic response capability of signal timing to the real-time traffic demand is remarkably improved, the peak delay of the intersection is effectively reduced by 20%, the average peak delay is effectively reduced by 15%, and the road network toughness is enhanced.

The invention belongs to the technical field of traffic flow green wave control, and particularly relates to a green wave control method based on traffic flow prediction driving. The method comprises the following steps: acquiring multi-dimensional real-time data in a distributed manner; performing abnormal value detection and data interpolation based on space-time neighborhood analysis to generate high-quality time sequence traffic flow data; modeling a road network into a dynamic graph fusing static connectivity and real-time traffic flow coupling degree, and constructing a double-time-scale time sequence diagram sequence; inputting an improved space-time diagram neural network, extracting time features by optimizing Bi-GRU, capturing spatial association by a multi-head attention mechanism, and outputting an accurate road section-level traffic flow prediction result through residual fusion; and finally, constructing a multi-objective optimization model based on prediction, solving by using an improved multi-objective particle swarm algorithm, obtaining a signal timing scheme, and issuing and executing the signal timing scheme. The method can dynamically adapt to traffic changes, improves the traffic efficiency, and avoids green wave failure and congestion.

The invention provides a bus signal priority control method considering coordination of a phase deviation state, and belongs to the technical field of vehicle-road cooperation. The method comprises the following steps: constructing an end-side-cloud architecture system for an intersection implementing bus signal priority control, acquiring basic data of the intersection and buses, establishing a signal priority control method considering coordination phase deviation state constraint, and calculating the maximum bus signal priority time of the intersection. And signal timing schemes under different priorities are generated in advance according to the priority time, so that the accuracy and the real-time performance of bus priority execution are ensured. According to the invention, a reliable control means for passing without stopping is provided for buses, interference to social vehicle passing benefits is reduced, the coordination offset time is fully utilized, the success rate of bus signal priority control is increased, the riding experience of passengers can be improved, the energy consumption of the buses can be reduced, and the cost is reduced. The method is of great significance to further development of an urban public transport system.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may select a zone configuration for a zone that is specific to where signal measurements satisfy a signal threshold, a signal timing satisfies a timing threshold, UE sensing measurements satisfy a sensing threshold, a UE mobility speed satisfies a speed threshold, a UE mobility direction satisfies a direction characteristic, or a signal angle of arrival satisfies an angle characteristic. The UE may communicate using the zone configuration based at least in part on a determination that the UE is located in the zone. Numerous other aspects are described.

The invention relates to the technical field of traffic signal control, and discloses a signal timing optimization method and system based on vehicle-road cooperation and traffic flow prediction, and the method comprises the steps: obtaining the track and road side detection data of a network-connected vehicle, dynamically estimating the permeability through Kalman filtering, and reconstructing the full traffic state of an intersection; predicting future traffic flow parameters by using a long-short-term memory network based on the total state historical sequence; establishing an optimization model aiming at minimizing delay and queuing, and solving by adopting a deep reinforcement learning algorithm to obtain a signal timing scheme; and issuing and executing the scheme, broadcasting a green light speed guide message, and feeding back online update model parameters based on an execution result. According to the method, the problem of state perception in a low permeability environment is solved, active prediction control of traffic signals and vehicle-road collaborative closed-loop optimization are realized, and the traffic efficiency of the intersection is effectively improved.

The invention discloses an urban intersection intelligent signal timing optimization method based on vehicle-road cooperation, and relates to the technical field of intelligent traffic, and the method comprises the specific steps: collecting intersection traffic data in real time through an Internet of Things equipment linkage system such as a geomagnetic sensor, and transmitting the intersection traffic data to a regional center after localization processing; fusing the data to predict the traffic flow situation, and generating a single-point dynamic timing scheme; a regional scheme is obtained through multi-intersection collaborative optimization; issuing and executing the scheme, collecting feedback data, and forming a closed-loop optimization system based on error optimization algorithm parameters; according to the invention, multi-source data acquisition and traffic flow prediction are fused, the traffic signal timing refinement and foresight are realized, and the traffic efficiency of a single-point intersection is improved; meanwhile, through a multi-intersection collaborative optimization and closed loop iteration mechanism, a global optimization system is constructed, single-point timing isolation is broken, regional traffic flow is efficiently scheduled, vehicle delay is reduced, and an effective scheme is provided for urban traffic control.

The invention discloses a distributed seat system operation state and fault monitoring method, which is suitable for scenes such as a command center and an enterprise conference room, and is used for solving the problems that hidden faults are difficult to find, fault response is lagged and the system stability is insufficient due to lack of a node local display device, single fault prompting mechanism and limited monitoring dimension of the existing system. According to the method, a low-power-consumption touch screen is configured at each node to synchronously display equipment states and signals, and multi-protocol compatible decoding is combined with load balancing to schedule the signals; constructing a hierarchical fault prompt mechanism and linking the operation and maintenance mobile terminal; an operation and maintenance management platform is built, multi-dimensional monitoring of equipment, links, machine room environment and power utilization is achieved, and edge calculation decoding, signal timing switching and equipment fault prediction are also supported. The method can improve the fault positioning efficiency, enhance the system operation stability, shorten the fault response time, realize full-scene management and control, and guarantee the continuous development of key businesses.

A memory device (e.g., a high-bandwidth (HBM) memory device) that includes a memory die having multiple pseudo channels per channel is disclosed. The memory die can include first memory banks associated with a first channel (e.g., having a first command address (CA) bus) and a first pseudo channel (e.g., having a first data (DQ) bus) and second memory banks associated with the first channel and a second pseudo channel (e.g., having a second DQ bus). Operations can be performed at the first memory banks or the second memory banks in response to a command received through the first CA bus. The operations can cause data to be returned to circuitry that routes the data to an interface to the first DQ bus or an interface to the second DQ bus based on whether the data resulted from operations at the first memory banks or the second memory banks.

The invention relates to the technical field of traffic monitoring. The traffic flow dynamic monitoring method and device, the equipment and the medium are provided, and the method comprises the steps that independent game nodes are arranged at key traffic intersections of a target road network in a distributed node deployment mode, and a remote game control framework is constructed; collecting real-time traffic flow data and associated environment parameters, and generating an initial signal control strategy; performing cross-regional information exchange on the initial signal control strategy to generate a traffic resource allocation game model; performing iterative optimization on the traffic resource allocation game model by adopting a distributed Nash equilibrium algorithm to generate a global coordination signal timing scheme; performing scenarized strategy customization on the global coordination signal timing scheme according to the road function partition features to obtain a scenarized signal control instruction; and issuing the scenarized signal control instruction to an execution terminal of a corresponding intersection so as to improve the multi-intersection collaborative decision-making capability, enhance the scenarized strategy adaptability and optimize the dynamic optimization real-time performance.

The application discloses a kind of flexible intersection traffic scheme planning method and computer program product.It includes: firstly, the time range corresponding to current planning phase, the traffic state information such as the position, speed, acceleration, driving lane and steering of all vehicles first entering control area are collected.Then, based on the traffic state information of all vehicles, the mixed integer linear programming optimization model set is solved, and the traffic scheme corresponding to current planning phase is obtained.The traffic scheme includes the real-time trajectory of each car in the time range of current planning phase, the length of each control partition of sub-intersection control area, lane function, the connection path of sub-intersection lane, the signal timing scheme of sub-intersection, and the traffic design scheme of sub-intersection.According to the traffic scheme, the coordinated control of lane division, trajectory planning, signal timing and partition division is realized, so as to realize the fine management of flat intersection, and maximize the intersection traffic efficiency.

The invention relates to the field of intelligent traffic, and particularly discloses a crossroad traffic passing control method based on multi-phase cooperative control, which comprises the following steps that four directions of a crossroad are divided into two opposite passing groups, the first group comprises a first crossroad and a third crossroad, and the second group comprises a second crossroad and a fourth crossroad; setting a first phase for simultaneous straight passing of each group of intersections, wherein the passing time is T seconds; a straight passing period is performed at each group of intersections; according to the invention, four intersection directions are divided into two groups, and the straight-going phase and the left-turning phase which alternately run are set, so that vehicles in any direction only need to wait for one phase period (35 seconds) at most to pass, and compared with the traditional multi-phase sequential passing, the efficient signal timing mode has the advantages that the traffic efficiency is greatly improved, and the traffic cost is reduced. Therefore, the average waiting time of the vehicle at the intersection is greatly shortened to 35 seconds from 105 seconds, the passing efficiency is greatly improved, and the traffic pressure in peak hours is effectively relieved.

The invention provides a signal-to-noise ratio estimation method and device suitable for PSK (Phase Shift Keying) modulation. The method comprises the following steps: calculating the average power of an input signal sequence; estimating the average power of the pilot frequency sequence; and calculating a signal-to-noise ratio. According to the method, the influence degree of noise on the distance between the PSK modulation signal and the original point in the constellation diagram is estimated, a traditional method for calculating the signal-to-noise ratio by estimating the deviation degree of the actual signal from the ideal signal due to the influence of noise is replaced, high accuracy can still be kept when the signal is influenced by Doppler frequency deviation and sampling deviation, the method is more suitable for an actual communication system, and compared with the prior art, the method has the advantages of simple operation and low cost. In an actual communication system, signal-to-noise ratio estimation is usually carried out after signal timing synchronization and carrier synchronization processing, however, the signal-to-noise ratio calculation method provided by the invention can be used for estimating the signal-to-noise ratio after synchronization before synchronization according to PSK modulation characteristics, and has certain application advantages in scenes such as switching modulation waveforms according to the signal-to-noise ratio.

The invention provides an urban intersection signal evaluation optimization method and device, equipment and a medium, and relates to the technical field of urban traffic. The method comprises the following steps: acquiring current traffic management data of a target intersection; wherein the traffic management data comprises traffic big data platform delay data and does not comprise traffic flow data; according to the traffic management data and an ideal optimal signal period model and an ideal delay model obtained by performing deformation derivation on the Webster delay model, calculating an ideal optimal signal period and an ideal delay of the target intersection; comparing the ideal delay with the traffic big data platform delay data, and judging whether the current signal of the target intersection needs to be optimized or not; and if optimization is needed, optimizing the current signal of the target intersection according to the ideal optimal signal period. According to the method, the intersection signal timing scheme can be evaluated and preliminarily optimized under the condition that traffic flow data are missing, and the efficiency and adaptability of traffic signal control are improved.

The invention relates to the technical field of early warning, and discloses a rock burst monitoring and early warning method based on microseismic signal time sequence characteristics. The method comprises the following steps: obtaining a first micro-seismic source set based on the position of a detector and a historical micro-seismic event; obtaining an associated detector corresponding to each micro-seismic source, and obtaining a first historical detection data sequence of the associated detector corresponding to the micro-seismic source in a first time window before a micro-seismic event occurs; performing clustering based on the first historical detection data sequence to obtain a plurality of clusters, and obtaining information entropy based on continuous historical detection data sequences in the clusters; obtaining a second historical detection data sequence based on a correlation detector; obtaining a first time sequence feature and a first information entropy value based on the second historical detection data sequence; determining an associated classification cluster based on the first time sequence feature; determining a first difference between the first information entropy value and the information entropy value corresponding to the classification cluster; and when the first difference is greater than a first threshold value, early warning is carried out on the micro-seismic event.

The application provides a traffic signal optimization control method and system, the method comprises the following steps: carrying out data preprocessing on original perception data to obtain real-time traffic state data; converting the real-time traffic state data into road qualitative running state based on a qualitative conversion grading table; constructing a traffic signal control knowledge graph, embedding and updating the real-time traffic state data to obtain updated embedding features, and encoding the knowledge units in the traffic signal control knowledge graph to obtain a target traffic signal control knowledge graph; determining a reasoning context based on a natural language query; determining a natural language enhanced prompt, inputting the natural language enhanced prompt into a pre-trained large language model to output a traffic signal control optimization strategy, and the application can realize accurate perception of complex traffic scenes, dynamic fusion of knowledge, and generation of an interpretable and highly reliable signal timing strategy.

The embodiment of the invention discloses a traffic network scheduling control method and system based on space-time big data and electronic equipment, and belongs to the technical field of control or regulation systems.The method comprises the steps that a first network state feature tensor representing a local space-time evolution mode is extracted from a space-time grid through a three-dimensional convolutional neural network, and aggregating node information on the dynamic weighting graph by using the graph attention network, generating a second network state feature tensor reflecting the global association and congestion propagation situation of the road network, fusing the two feature tensors, and inputting the fused feature tensors into an attention enhancement sequence model for multi-step prediction to obtain a future multi-period whole network traffic state. And cooperatively inputting the prediction result, the real-time state, the rule and the capacity constraint into the optimization model, generating a comprehensive scheduling instruction set including signal timing, lane control and path induction, and issuing and executing the comprehensive scheduling instruction set. According to the embodiment of the invention, holographic perception, prospective prediction and cooperative regulation and control of the traffic network are realized, and the traffic efficiency and scheduling response capability of the road network are improved.

The invention discloses a green wave coordination control method and device and electronic equipment, and belongs to the technical field of traffic. The method comprises the steps that operation constraint conditions and coordination directions of all intersections on a trunk road are determined, a trunk road coordination control scheme meeting the coordination directions of all the intersections is determined under the operation constraint conditions, and the coordination control scheme at least comprises signal timing schemes of all the intersections on the trunk road; for any intersection on the trunk road, the signal timing scheme of the intersection is optimized through the optimization function; and controlling the intersection according to the optimized signal timing scheme so as to realize green wave coordination control of the trunk road. According to the invention, the control effect of green wave coordination control on the trunk road is improved.

The CAM array of compare memory cell circuits includes a decode column corresponding to each set, with each set including at least one row of compare memory cell circuits. Each decode column receives a set clock signal addressing a corresponding set and generates a set match signal for each row of the corresponding set. A column compare circuit generates compare data indicative of bits of a compare tag. A row match circuit generates, for each row, a row match signal in response to the set match signal indicating that the compare tag matches the binary tag stored in that row. The circuitry and loads in the decode column used to generate the set clock signal correspond to the circuitry generating the row match signal in each column of the CAM array, thereby reducing timing margins for match indications and shortening access times to the CAM array.

The application provides a ramp traffic signal adaptive control method and system based on unmanned aerial vehicle cooperation, belongs to the technical field of intelligent traffic control, and comprises the following steps: collecting traffic data in real time based on an unmanned aerial vehicle cluster dynamically arranged above a ramp, constructing a global traffic state matrix, and performing preliminary processing through edge computing; generating a local optimal signal timing suggestion in real time through edge computing of the edge node on the preliminarily processed data; integrating and uniformly training the local optimal signal timing suggestions uploaded by multiple unmanned aerial vehicles on the cloud to generate a global optimal signal timing strategy; and adjusting the signal lamp parameters in real time based on the global optimal signal timing strategy, thereby enhancing the direct intervention ability on the on-site traffic through the air guidance function and providing a high-reliability solution for a smart traffic system.

The present invention relates to a method for performing two-phase detection on abnormal congestion on an urban road on the basis of the polar coordinate transformation of trajectory data. The method includes two phases, i.e., the identification of an abnormal trajectory segment and the detection of incident-induced congestion. In the phase involving the identification of an abnormal trajectory segment, two key features (i.e., an average speed and a time when a road section is entered) are defined so as to capture trajectory segment features within spatio-temporal windows, and the features are affected by both incident-induced congestion and signal timing at downstream intersections; and on the basis of the two key features, the abnormal trajectory segment is identified by means of clustering. In the phase involving the detection of incident-induced congestion, anomaly rates of the spatio-temporal windows are defined; and on the basis of the anomaly rates, a decision tree is used to identify spatio-temporal windows in which incident-induced congestion occurs. Compared with the prior art, the present invention is applicable to urban road networks, and by means of the present invention, the detection of incident-induced congestion at a signal-cycle level in time and a vehicle-flow-level in space is realized.

The invention relates to a multi-lane confluence control method and system based on real-time feedback and double-layer optimization, and the method comprises a double-layer decision step: when a downstream confluence interleaving region is judged to be in a congestion state, firstly, according to the congestion state and the length of the interleaving region, dynamically determining a flow limiting strategy and a target release flow of main road confluence entering the cross section of the interleaving region; and then, the lower-layer planning model re-optimizes an upstream intersection signal timing scheme under the condition of meeting the flow limiting constraint condition, and controls the upstream arrival traffic flow of the interleaving area through a method of controlling the green signal ratio and the period through the signal entering the bottleneck section direction of the upstream intersection. According to the method, multiple objectives such as queuing constraint after interleaving area entrance control, non-control direction delay minimization of an upstream intersection after interleaving area association control constraint, interleaving area dissipation capability maximization and the like can be comprehensively considered, and prediction control is performed through a double-layer optimization model; the maximum traffic capacity of the trunk road confluence area is achieved, and the influence on upstream road traffic is minimum.