EP2280383A1 - Procédé d'établissement d'informations de circulation pour un traject routier d'un réseau routier et calculateur de circulation destiné à l'exécution du procédé - Google Patents

Procédé d'établissement d'informations de circulation pour un traject routier d'un réseau routier et calculateur de circulation destiné à l'exécution du procédé Download PDF

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Publication number
EP2280383A1
EP2280383A1 EP09167020A EP09167020A EP2280383A1 EP 2280383 A1 EP2280383 A1 EP 2280383A1 EP 09167020 A EP09167020 A EP 09167020A EP 09167020 A EP09167020 A EP 09167020A EP 2280383 A1 EP2280383 A1 EP 2280383A1
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Prior art keywords
model
traffic
traffic flow
time
measurement data
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EP09167020A
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German (de)
English (en)
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EP2280383B1 (fr
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Jürgen Mück
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Siemens AG
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Siemens AG
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Priority to PL09167020T priority Critical patent/PL2280383T3/pl
Priority to EP09167020A priority patent/EP2280383B1/fr
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/081—Plural intersections under common control

Definitions

  • the invention relates to a method for determining traffic information for a road section of a road network and to a traffic computer for carrying out the method, to a machine-readable program code for the traffic computer and to a storage medium with program code stored thereon.
  • the determination of traffic information for traffic signal controlled roads is possible with high manual effort by observation.
  • the measurement data of the commonly used vehicle detectors for traffic control usually 10 to 40 m in front of a traffic signal arranged induction loops, can not or only partially be used to accurate traffic information about the number of stopping vehicles, waiting times or in the manual for the design of Road traffic facilities, known by the short name HBS, defined levels of traffic quality to win.
  • HBS Road traffic facilities
  • a method for determining a travel time for a vehicle in a route network that can be subdivided into sections is known.
  • the route network is subdivided into route sections such that a route section ends downstream on a traffic light system controlling the traffic flow on this route section.
  • the traffic flow characterizing traffic data is detected by means of a road-side measuring device. From the temporal behavior of the continuously recorded traffic data and the derived current traffic situation on this section, a travel time for this section is calculated.
  • the data collected for jam lengths and travel times are accurate enough to be used for control measures in a traffic management system, they are not used for expert opinions.
  • the method does not provide any information about the coordination quality of successive traffic signal systems.
  • EP 0 501 193 A1 is a method for an automatic traffic coordination of an independent node control unit of a traffic signal system with adjacent nodes known.
  • the traffic flowing in from there is detected and analyzed, wherein the number of vehicles per time interval is determined and stored for a given measurement period with a plurality of time intervals.
  • the measuring period is divided into test cycles with the same cycle time. Within the test cycles, the number of vehicles per respective time interval is added up.
  • Such a mapping of the entire measurement period to a test cycle allows averaging and variance formation. For further test cycles, each with different cycle times, such mappings and variance calculations are made. Since the magnitude of the variance is a measure of the cyclic property of the incoming traffic, an existing vehicle cycle and its cycle time for the coordinated control of the signaling system is determined from the calculated variances.
  • the European patent application EP 1 276 085 A1 shows a method for determining a congestion indicator and for determining tailback lengths.
  • a method for determining a congestion number at operator stations for handling individually moved units is disclosed.
  • two methods for estimating the back-up length at the operating station are obtained.
  • the first method exploits a linear relationship between backpressure length and smoothed congestion figure.
  • the slope of the queue length function is calibrated by comparing the current traffic jam number with a lower limit for the traffic jam length.
  • the backlog length is calculated from the traffic jam count and the saturation time requirement using a macroscopic queuing model.
  • the publication DE 101 08 611 A1 shows a method for simulating and predicting the movement of individual vehicles on a network of traffic routes with network nodes and these connecting sections by microscopic quantities using currently measured and historical traffic data.
  • macroscopic traffic variables are determined and in a further step, the microscopic individual vehicle sizes are generated separately for each vehicle.
  • On-line evaluation of green waves A fuzzy expert system for estimating the loss time before light signals by means of detectors near the line
  • Braun, Mück discloses a method for online evaluation of the coordination quality between two traffic signals. It needs a stop near detector in front of the downstream traffic signal system, which measures the occupancy rate every second and counts the number of vehicles.
  • the current signal position of this traffic light system must be known. From the detector data and the signal position six characteristic quantities are determined for each circulation.
  • a fuzzy expert system was developed, which estimates the average loss time from these quantities per revolution. This can be used for unconstrained undisturbed traffic conditions on the coordination quality be closed between this and the upstream traffic signal system.
  • a fuzzy expert system makes it possible to linguistically formulate complex relationships. For the present problem, this approach is very well suited, since the traffic-technical relationships between the characteristic quantities and the loss time can be linguistically formulated, and processed as expert knowledge.
  • the online evaluation method of coordination quality can be used to verify the quality of existing green waves.
  • a use in the context of traffic-dependent controls of traffic lights is basically conceivable, since the method works under real-time conditions.
  • the method does not provide any information about the compositions of the vehicle spool in the inflow.
  • the method is complex and contains a lot of heuristics. It does not include a historical history of the data and thus omits information. Finally, the accuracy of the procedure is not suitable for expert opinion and quality assurance.
  • the EP 1 480 183 A1 Fig. 12 shows a method for determining traffic characteristics at operator stations for handling individual moving units with alternating blocking and transmission phases and with a detector located in front of the operating station with the steps of providing the points of a fundamental diagram for the operating station using the detector data and determining at least one Subset of points of the fundamental diagram corresponding to a traffic condition.
  • the invention is therefore based on the object to provide a method for determining traffic information and a traffic computer for performing the method, which in an automated manner an accurate determination of traffic information, such as waiting times and stops of individual vehicles, quality characteristics, and the like, is possible.
  • the object is achieved by a method for the determination of traffic information for a road section of a road network, the road section an entry cross section, through which a road route inquiring traffic flows, an exit cross section through which flows a controlled by a traffic signal traffic flow, and at least one between Entry and exit cross-section arranged measuring cross section at which a vehicle detector detected by passing vehicles generated measurement data, wherein the flow of traffic along the road route simulated by means of a traffic model is generated and depending on an incoming model traffic flow of the measured data associated model measurement data, wherein the incoming model traffic flow is varied with respect to the time distribution of entering the model roadway model vehicles and with respect to a match of each generated model measurement data is optimized with the corresponding measurement data acquired by the vehicle detector, and wherein the traffic information is determined from the simulated model traffic flow resulting from the optimized model traffic stream.
  • the simulation of the traffic flow takes place, for example, on the basis of a macroscopic traffic model which is known per se, wherein the modeling of the traffic flow can take place, for example, over surfaces of constant traffic density moving along the modeled road route.
  • the method according to the invention is therefore based on a simulative simulation of the real traffic flow and the resulting measurement data by means of a suitable traffic model.
  • the simulated model traffic flow is modified in its generation characteristic at the entrance cross-section - ie the time distribution of vehicles entering through the entry cross section - until the modeled measurement data generated by it are as similar as possible to the measured data actually measured.
  • the optimized incoming model traffic flow, or the resulting simulated model traffic flow then serves to derive the sought-after parameters, which are customary in practice, as traffic information.
  • Essential for the inventive method is not to model the incoming traffic flow via an upstream light signal system, but via a targeted modeling of the incoming traffic flow. With the obtained traffic information, it is possible to automatically determine or evaluate traffic parameters as well as the quality of traffic signal controls and green waves with little effort and without carrying out test drives.
  • a count of detected vehicles per time interval and an occupancy value of the vehicle detector per time interval are detected, with count and occupancy values are determined from raw data of the vehicle detector.
  • An essential point here is the first systematic evaluation of the raw data of the vehicle detector in the form of time data of its rising and falling edges or in the form of finely resolved counts and occupancy values every second for the determination of the coordination quality.
  • the detector edge data these are evaluated in a suitable, derived from the physics of traffic flow manner by the distance behavior and the transit time via the vehicle detector for each vehicle in macroscopic characteristics that may be finer in time than the time intervals of the investigation period, converted and possibly smoothed.
  • the use of the raw data enables, in comparison to the previously known methods, the exact determination of results in examination periods which are considerably shorter than hitherto customary, for example 10 to 30 minutes.
  • this method in contrast to comparing direct edge data from the model and the measurement, no further smoothing is needed to calculate the goodness of the match. By avoiding smoothing, the process becomes considerably more accurate.
  • the measurement data generated in this way in accordance with the count and occupancy values determined by macroscopic models, are significantly better than directly smoothed raw data.
  • the inflowing model traffic flow is respectively related to a circulation time of a signal time schedule running in a traffic light system controlling the incoming traffic flow.
  • the fact is taken into account that a light signal-controlled incoming traffic flow, the vehicles enter in orbital periodic pulses through the entrance cross section in the road section.
  • the inflowing model traffic flow based on the real orbital period of the inflow controlling traffic signal system.
  • the measurement data acquired during the time intervals of the examination period are allocated correspondingly to the individual signal cycles of the signal time schedule. If the time intervals of the examination period are already adapted to the signal cycles of the signal time schedule, the measurement data can be used directly. If the signal circulations overlap the time intervals with different time durations and / or starting times, then the measurement data of a time interval must be divided up in relation to its overlap with the signal circulations. Thus, the measurement data are tuned to the signal circulations of the incoming traffic flow stamping traffic signal.
  • the model traffic flow flowing in during a circulation time is formed by multiplying the sum of the counted values (z i ) associated with circulation with detected vehicles (F) having a normalized pulse profile (p ') which has a time distribution of Vehicle proportions of an inflowing vehicle pulse within a cycle time indicates, wherein the inflowing model traffic flow is varied by varying the underlying pulp profile.
  • the bulk profile is used as the central, to be estimated characteristic of the method according to the invention.
  • the pulse profile includes the duration of a round trip time of the signal time schedule of the inflow controlling traffic signal system.
  • the pulse profile is normalized, for example, to a unit area and indicates in which time periods of a circulation time of the incoming traffic flow controlling traffic signal, which proportion of the total number of vehicles retracted during the orbital period through the entrance cross section into the stretch of vehicles.
  • the formation of the model traffic flow flowing in during the investigation period is based on the same pulse profile for each revolution time. This assumption is justified for sufficiently short examination periods, for example up to one hour, and simplifies the method by using a constant pulse profile for all signal circulations.
  • the traffic flow is simulated by simulating movements of model vehicles of the incoming model traffic flow along a model roadway, which generate model measurement data when passing a model measurement cross section and which by a light signal controlled model exit cross section flow away.
  • a microscopic traffic model based on a targeted insertion of individual model vehicles at the model entrance cross-section provides detailed traffic information.
  • the inflowing model traffic flow is formed from model vehicles of different vehicle classes, each with a mean vehicle length, wherein a composition of the model traffic flow from vehicle classes and their average vehicle lengths are predetermined or varied.
  • the incoming model traffic flow can be examined not only in terms of its time distribution during a round trip time, but also in its composition with respect to different vehicle classes, such as passenger cars, trucks or buses, which have different acceleration and cruising speed values.
  • characteristic values for different vehicle classes are also available.
  • the traffic model used for the simulation can be calibrated.
  • the repetitive cycle time or a sequence of changing cycle times of the signal time schedule which takes place in the light traffic system controlling the incoming traffic flow, is varied, the acquired measurement data being correspondingly associated with the repetitive cycle time or the changing cycle times become.
  • This method can be used with advantage in uncertainty with respect to the coincidence of the transit times of the transmitting and considered traffic signal system.
  • a simple pulp profile composed of, for example, a main direction pulp and a pitching pulse may be presumed so that the optimization is limited to determining the revolutions of the transmitting traffic signal.
  • a distance between the respectively generated model measurement data and the corresponding measurement data acquired by the vehicle detector is calculated for at least a portion of the time intervals and an average value of the distances for the portion of the time intervals of Examination period minimized. If the traffic volume in the units of vehicles per hour and the occupancy rate in percent are present as measurement data per time interval, the square root could be formed as the distance measure for a specific time interval from the sum of the squares of the differences between the real measurement data and the model measurement data. From all distance measures of the time intervals of an examination period, an arithmetic mean value is now formed which is minimized iteratively by varying the pulse profile generating the model measurement data.
  • the pulse profile on which the incoming model traffic stream is based is varied by using genetic algorithms.
  • This method which is known per se, is particularly suitable for the present application, iteratively adapting a normalized burst profile.
  • a degree of correspondence of the model measured data generated by the latter with the corresponding measured data detected by the vehicle detector is determined, wherein from the variants exceeding a predetermined threshold for the quality measure Bandwidth of possible variation parameters is determined.
  • movement lines and / or holding and / or waiting times of the model vehicles are determined from the optimized incoming model traffic flow by statistical evaluation of the model vehicle movements as traffic information.
  • the statistical evaluation of the simulated traffic flow makes it possible to determine waiting times and stops of all vehicles, possibly differentiated according to vehicles which have flowed in from the main direction or from a secondary direction. From the shape of the found pulp profile, the Pulehenile the main direction and the bending secondary directions can be assigned. If it is noted in the simulation which vehicles were generated from the main directional part of the pulp profile, their travel profiles can be evaluated separately after the simulation. It is even a distinction of vehicles of the main direction in those who are in the road section possible at green start or during a later time of the green time.
  • a quality value for the road route is calculated as traffic information.
  • This may be the so-called “level-of-service” quality score set out in the Road Safety Assessment Manual (HBS).
  • HBS Road Safety Assessment Manual
  • an optimal coordination of the traffic light system at the exit cross section to the traffic signal at the entrance cross section is determined.
  • Essential here is the offset of the two signal time schedules to achieve a green wave. If waiting times and stops of the main and secondary direction vehicles are weighted, a recommendation for an optimal shaft position or coordination on this road section can be given via a downstream optimization algorithm; it can also be determined by what percentage the current situation is away from the optimum.
  • threshold values are specified for holding and / or waiting times and / or coordination deviations, whose overshoots or undershoots are determined during an analysis period and reported after the analysis period as quality analysis.
  • the evaluation it is possible to identify from several access roads those in which the controls of the traffic signals should be checked for their quality.
  • automated mechanisms can be used to perform a quality check in the background and, for example, generate a quality and abnormality analysis with a report on traffic quality and abnormalities after one day.
  • the road section has a plurality of lanes, wherein at least one lane at least one lane-related vehicle detector, wherein the inflowing model traffic flow with respect to the temporal distribution of entering into the respective model lanes of the model road route model vehicles varies and is optimized with respect to a match of the respectively generated model measurement data with the corresponding measured by the at least one lane-related vehicle detector measurement data.
  • the method can also be used for more complex node topologies, wherein the estimate may include several lanes per road section. The individual lanes may have none, one or more consecutive vehicle detectors.
  • a traffic computer for determining traffic information for a road section of a road network, which is provided with a program code containing control commands that cause the traffic computer to carry out a method according to one of claims 1 to 16.
  • the traffic computer has correspondingly designed data processing means, interfaces for data input and output as well as a visualization unit for displaying the traffic information.
  • the invention relates to a machine-readable program code for a traffic computer, which contains control commands that cause the traffic computer to carry out a method according to one of claims 1 to 16.
  • the invention also relates to a storage medium having a machine-readable program code stored thereon according to claim 18.
  • FIG. 1 shows a road section s 12 of a road network, for example, connects two nodes not shown.
  • the road section s 12 has an entry cross-section 1 at the preliminary node, an exit cross-section 2 at the main node and an interposed measuring cross-section 3.
  • a traffic route querying the road s 12 formed by vehicles F, to.
  • the incoming traffic flow is controlled by a traffic signal 10 at the Vorknoten.
  • the traffic signal system 10 has signal transmitters 11 for the main traffic flow and the secondary traffic flows whose signal times are switched according to a signal schedule SP 1 running in the control device 12.
  • the inflowing traffic flow takes place in vehicle pulse per revolution time of the signal time schedule SP 1 .
  • detector raw data in the form of count values z i and occupancy values b i are detected at the measuring cross-section 3 by a vehicle detector 30, which is designed as an induction loop, for example, at equidistant time intervals i.
  • edge data ie those times at which the occupancy state of the vehicle detector 30 of "occupied (value 1)” to “not busy (value 0)” changes and vice versa.
  • edge data ie those times at which the occupancy state of the vehicle detector 30 of "occupied (value 1)" to "not busy (value 0)” changes and vice versa.
  • a vehicle F leaves the detection range of the vehicle detector 30.
  • a time gap h 1 , h 2 or h 3 arises until the next vehicle F enters the detection range of Vehicle detector 30 retracts.
  • the subsequent occupancy time o 1 , o 2 or o 3 then ends at the next falling edge t 2 , t 3 or t 4 .
  • edge data 30 and every second resolution occupancy states of the vehicle can be used as raw data detector may be used, out of which also the time data t i, h give i, o i.
  • the count value (dash-dotted line) and the occupancy value (solid line) are assigned to a discrete time axis with equidistant time intervals i of, for example, one second.
  • the count value of z i is the number of vehicles per second, in the time interval i, while the occupancy value b i indicating the holding time per second in the time interval i.
  • the detector raw data are summarized according to the signal positions circulating for the incoming traffic flow, wherein the cycle time of the signal time schedule SP 1 can be for example 60 s or 90 s.
  • the cycle time of the signal time schedule SP 1 can be for example 60 s or 90 s.
  • a traffic signal 20 which has a signal generator 21 and a controller 22, in which emitted by the signal generator 21 light signals are switched according to an expiring signal time plan SP 2 .
  • the traffic signal systems 10 and 20 at the entrance cross-section 1 and exit cross-section 2 by adjustment the same cycle times of the signal time schedules SP 1 and SP 2 coordinated; the signal cycles of the signal time schedules SP 1 and SP 2 are offset in time according to the length of the road section s 12 and the typical driving speeds on the road s 12 .
  • the choice of the offset time is decisive for the quality of the coordination of the two traffic signal systems 10 and 20, respectively.
  • a simulation of the traffic flow 12 now runs along the road segment s by means of a traffic model VM off by a Pulkprofil p 'of the incoming traffic flow model to z' is estimated iteratively.
  • a pulp profile p ' is in FIG. 6 shown.
  • the pulp profile p ' indicates over a cycle time of, for example, 90 s, the time course of the proportion of vehicles F, which retract per period of time through the inlet cross-section 1.
  • the entire pulp profile p ' is normalized by dividing by the total number of vehicles F passing the entrance cross section 1 during the circulation time. Over an examination period of, for example, one hour, it can be assumed that the pulse profile p 'is constant for every 40 time intervals or circulation times of 90 s in each case.
  • the optimization with the help of the traffic model VM now follows as follows:
  • the movements of model vehicles of the incoming model traffic flow z are now simulated to 'along a model road route, which generate model measurement data z i ' or b i 'when passing a model measurement cross section and which by a Departure light signal controlled model exit cross section.
  • the traffic model VM the real signal cycles of the signal time schedule SP 2 of the traffic light control system 20 controlling the outflow enter.
  • traffic models VM are known to those skilled in the art.
  • microscopic traffic models aimed at tracking individual model vehicles are in use here.
  • the traffic model VM provides model measurement data in the form of model counts z i 'and model occupancy values b i ', which are then compared to the measurement data z i and b i actually generated by the vehicle detector 30 in the respective time intervals i.
  • the distance measure d i used is the Euclidean distance between the corresponding real and model-generated points in the fundamental diagram, in which for each time interval i the traffic volume q in vehicles per hour is plotted over the occupancy rate b in percent.
  • the mean value d of the distance measures d i is now compared with a threshold value D. As long as the mean value d exceeds the threshold value D, the pulse profile p 'is adapted using genetic algorithms GA and new model measurement data z i ' or b i 'is generated by means of the traffic model VM until the mean value d of the distance measures d i reaches the threshold value D reaches or falls below.
  • the iteration procedure can also be aborted if a predefined runtime is exceeded or if the mean value d only changed by small values. In this case, the optimized model traffic flow z to 'was determined, which best simulates the real measured data z i or b i .
  • the traffic information VI can be obtained from the simulated model.
  • Traffic flow which results from the optimized model traffic flow z to 'can be determined.
  • SA statistical evaluation SA of the model vehicle movements of the optimized incoming model traffic flow z to ', for example, movement lines, stops and waiting times of the model vehicles are determined.
  • a quality characteristic value for the road route s 12 is calculated.
  • the optimal coordination between the traffic signal systems 10 and 20 at the exit section 2 or entrance cross section 1 can be determined.
  • FIG. 7 a diagram of how the mean d of the distance measures d i changes as a function of the change in the offset time between the signal time schedules SP 1 us SP 2 .
  • the fictitious example assumes a given coordination of 60 s offset time.
  • the coordination was varied over one revolution in steps of 10 s, whereby a clear minimum of the mean distance d at an offset of 60 s modeled in the traffic model VM is recognized.

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EP09167020A 2009-07-31 2009-07-31 Procédé d'établissement d'informations de circulation pour un traject routier d'un réseau routier et calculateur de circulation destiné à l'exécution du procédé Not-in-force EP2280383B1 (fr)

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PL09167020T PL2280383T3 (pl) 2009-07-31 2009-07-31 Sposób określania informacji o ruchu drogowym dla odcinka drogi w sieci drogowej jak również komputer sterujący ruchem drogowym do realizacji sposobu
EP09167020A EP2280383B1 (fr) 2009-07-31 2009-07-31 Procédé d'établissement d'informations de circulation pour un traject routier d'un réseau routier et calculateur de circulation destiné à l'exécution du procédé

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EP09167020A EP2280383B1 (fr) 2009-07-31 2009-07-31 Procédé d'établissement d'informations de circulation pour un traject routier d'un réseau routier et calculateur de circulation destiné à l'exécution du procédé

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DE102011005495A1 (de) * 2011-03-14 2012-09-20 Siemens Aktiengesellschaft Verfahren und Steuerungssystem zur Verkehrsflusssteuerung
CN103680157A (zh) * 2014-01-06 2014-03-26 东南大学 一种面向城市瓶颈路段的车辆排队溢流预判方法
CN103927892A (zh) * 2014-04-29 2014-07-16 山东比亚科技有限公司 一种交通溢流协调控制优化模型的建立方法及其工作方法
CN105913666A (zh) * 2016-07-11 2016-08-31 东南大学 一种快速道路主线可变限速标志优化布设方法
CN109284527A (zh) * 2018-07-26 2019-01-29 福州大学 一种城市路段交通流仿真的方法
CN111199247A (zh) * 2019-12-25 2020-05-26 银江股份有限公司 一种公交运行仿真方法
DE102019209279A1 (de) * 2019-06-26 2020-08-13 Continental Automotive Gmbh Verfahren zum Betreiben eines Signalanlagensystems und Signalanlagensystem
CN112541465A (zh) * 2020-12-21 2021-03-23 北京百度网讯科技有限公司 一种车流量统计方法、装置、路侧设备及云控平台
CN116543562A (zh) * 2023-07-06 2023-08-04 银江技术股份有限公司 干线协调优化模型的构建方法和装置

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CN105931474A (zh) * 2016-02-29 2016-09-07 南京航空航天大学 一种带有量子决策的防止城市道路交叉口群的局部溢流控制方法

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DE102011005495A1 (de) * 2011-03-14 2012-09-20 Siemens Aktiengesellschaft Verfahren und Steuerungssystem zur Verkehrsflusssteuerung
CN103680157A (zh) * 2014-01-06 2014-03-26 东南大学 一种面向城市瓶颈路段的车辆排队溢流预判方法
CN103680157B (zh) * 2014-01-06 2015-09-16 东南大学 一种面向城市瓶颈路段的车辆排队溢流预判方法
CN103927892A (zh) * 2014-04-29 2014-07-16 山东比亚科技有限公司 一种交通溢流协调控制优化模型的建立方法及其工作方法
CN105913666A (zh) * 2016-07-11 2016-08-31 东南大学 一种快速道路主线可变限速标志优化布设方法
CN109284527B (zh) * 2018-07-26 2022-06-10 福州大学 一种城市路段交通流仿真的方法
CN109284527A (zh) * 2018-07-26 2019-01-29 福州大学 一种城市路段交通流仿真的方法
DE102019209279A1 (de) * 2019-06-26 2020-08-13 Continental Automotive Gmbh Verfahren zum Betreiben eines Signalanlagensystems und Signalanlagensystem
CN111199247A (zh) * 2019-12-25 2020-05-26 银江股份有限公司 一种公交运行仿真方法
CN111199247B (zh) * 2019-12-25 2023-11-10 银江技术股份有限公司 一种公交运行仿真方法
CN112541465A (zh) * 2020-12-21 2021-03-23 北京百度网讯科技有限公司 一种车流量统计方法、装置、路侧设备及云控平台
CN116543562A (zh) * 2023-07-06 2023-08-04 银江技术股份有限公司 干线协调优化模型的构建方法和装置
CN116543562B (zh) * 2023-07-06 2023-11-14 银江技术股份有限公司 干线协调优化模型的构建方法和装置

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