CN105894814B - Consider a variety of traffic control measure combined optimization methods and system of environmental benefit - Google Patents
Consider a variety of traffic control measure combined optimization methods and system of environmental benefit Download PDFInfo
- Publication number
- CN105894814B CN105894814B CN201610310512.7A CN201610310512A CN105894814B CN 105894814 B CN105894814 B CN 105894814B CN 201610310512 A CN201610310512 A CN 201610310512A CN 105894814 B CN105894814 B CN 105894814B
- Authority
- CN
- China
- Prior art keywords
- traffic
- road
- control
- flow
- traffic control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 30
- 230000007613 environmental effect Effects 0.000 title claims abstract description 26
- 230000008901 benefit Effects 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000000694 effects Effects 0.000 claims abstract description 27
- 238000007781 pre-processing Methods 0.000 claims abstract description 9
- 238000004088 simulation Methods 0.000 claims description 54
- 239000003344 environmental pollutant Substances 0.000 claims description 42
- 231100000719 pollutant Toxicity 0.000 claims description 42
- 238000011156 evaluation Methods 0.000 claims description 21
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000004422 calculation algorithm Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000008859 change Effects 0.000 claims description 10
- 230000014509 gene expression Effects 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 5
- 230000002068 genetic effect Effects 0.000 claims description 3
- 239000002245 particle Substances 0.000 claims description 3
- 238000002922 simulated annealing Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 abstract description 5
- 230000006855 networking Effects 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 37
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 10
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 6
- 229910002092 carbon dioxide Inorganic materials 0.000 description 5
- 239000001569 carbon dioxide Substances 0.000 description 5
- 229910052799 carbon Inorganic materials 0.000 description 4
- 230000003631 expected effect Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 229910002091 carbon monoxide Inorganic materials 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 2
- 239000000356 contaminant Substances 0.000 description 2
- 229930195733 hydrocarbon Natural products 0.000 description 2
- 150000002430 hydrocarbons Chemical class 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- -1 particulates Natural products 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
Classifications
-
- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Landscapes
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The present invention discloses a kind of a variety of traffic control measure combined optimization methods and system for considering environmental benefit.All vehicles access network, Che Yuche, Che Yulu, car and traffic control system in system, it is possible to achieve the transmission of data and information.System includes control range delimitation and Expected Results setting module, and data acquisition module, emulates data preprocessing module, Macro-traffic Flow emulation module, traffic control effect assessment module, design approach module, control program issue and implementation module.System utilizes the mass data information that car networking technology is provided, Macro-traffic Flow is simulated on emulation platform, by the combined optimization to a variety of traffic control measures, while road network operational efficiency is taken into account, road network automotive emission is reduced, achievees the purpose that to improve air quality.
Description
Technical Field
The invention belongs to the technical field of traffic environment engineering, and particularly relates to a method and a system for jointly optimizing multiple traffic control measures by considering environmental benefits.
Background
With the rapid development of economy in China and the continuous improvement of the living standard of people, the vehicle purchasing demand is steadily increased, and the quantity of motor vehicles kept is increased year by year. According to statistics of the traffic administration of the Ministry of public Security, the number of motor vehicles in the country reaches 2.79 hundred million by 2015, wherein 1.72 million automobiles are reserved, and the new registration amount and the annual increment reach the highest level of history. The increase of the quantity of motor vehicles brings convenience to people to go out, and meanwhile, a series of problems such as traffic jam and environmental pollution can be caused. In 2015, in the national environmental monitoring conference, the subsidiary ministry of the environmental protection agency refers to the analysis results of atmospheric pollution sources of 9 cities, and the main atmospheric pollution sources of four cities of Beijing, Hangzhou, Guangzhou and Shenzhen are determined to be motor vehicles. Therefore, how to reduce traffic emission and improve air quality through scientific traffic management and reasonable control measures becomes a problem to be solved urgently.
The traffic control measures mainly include: road section and steering forbidding, signal timing, road tolling, etc. In the research and practical application of predecessors, the above means are mainly used for improving the operation efficiency of the road network. However, as environmental problems become more prominent, it is necessary to apply them to traffic control considering environmental benefits. Although the scholars at home and abroad make a lot of effective researches on the traffic emission control problem, the mutual balance between traffic efficiency and environmental benefit and the joint optimization of management and control measures are only mentioned, and the increasingly popular car networking technology is not fully utilized.
Disclosure of Invention
The invention aims to provide a method for jointly optimizing various traffic control measures in consideration of environmental benefits aiming at the defects of the prior art, and the method reduces the exhaust emission of motor vehicles in a road network and improves the air quality while considering the operation efficiency of the road network by utilizing mass data information provided by the Internet of vehicles and carrying out joint optimization on various traffic control measures.
The purpose of the invention is realized by the following technical scheme: a method for jointly optimizing a plurality of traffic control measures in consideration of environmental benefits comprises the following steps:
(1) the traffic manager determines the extent of the control area, the traffic assessment index, and the expected control effect. The traffic evaluation index can reflect the overall operation condition of the road network and give consideration to the operation efficiency and the environmental benefit of the road network.
(2) And obtaining the information of the road, vehicle, environment and traffic control scheme in the control area.
(3) And (4) preprocessing macroscopic traffic flow simulation input data.
And (3.1) counting the total number of the road sections and the total number of the nodes in the control area defined in the step (1), and numbering each road section and each node in sequence.
And (3.2) determining the type of the vehicle to be controlled in each traffic mode, and reversely deducing the traffic demand in the control area according to the traffic flow information obtained in the step (2), wherein the traffic demand takes the number of people as a basic unit. The traffic demand is expressed in the form of OD array, described by two-dimensional array Q, and for each element Q [ i ] [ j ] in the array, the mark i represents a starting point, the mark j represents a terminal point, and the value of Q [ i ] [ j ] represents the traffic demand in unit time from i to j.
And (3.3) deducing the traffic capacity C of each road section in the control area under different traffic modes according to the road information and the traffic flow information obtained in the step (2).
And (3.4) acquiring the free flow speed v of the road section according to the road information. Estimating free flow time t of each road section in different transportation modes according to the free flow speed v and the road section length L obtained in the step (2)0＝L/v。
(3.5) according to the road section traffic capacity C and the free flow time t0And designing time road resistance t of the road section under different traffic modes k. Wherein t is a function of the traffic flow x of the traffic mode k on the road section a, and the function satisfies the following condition: monotonically increasing and continuously differentiable; when the flow is extremely small, the path resistance is close to zero flow impedance; allowing for the presence of a supersaturated flow.
And (3.6) determining the traffic control measures to be optimized and the specific road sections or intersections implemented by the traffic control measures, and recording the current control scheme. The traffic control measures include but are not limited to road section and steering forbidding, signal timing, road charging, road section canalization, tide lane and traffic priority setting.
And (3.7) determining the influence of the traffic control measures to be optimized on the time road resistance function, and adding the influence into the mathematical expression of the time road resistance function in a control (variable form) manner to obtain a generalized road resistance function c.
And (3.8) determining the type of the pollutant to be controlled according to the information of the concentration of the pollutant in the air measured in the step (2). The evaluation index of the pollutants can be the degree of harm of the pollutants to human health or the degree of influence of the pollutants on air visibility.
And (3.9) estimating the average emission factor of the pollutants needing to be controlled in the step (3.8) in the range of the control area under different transportation modes according to the average emission amount of the pollutants in each vehicle unit time obtained in the step (2).
(4) Macroscopic traffic flow simulation
② and ② (② 4.1 ②) ② inputting ② the ② data ② obtained ② in ② the ② step ② (② 2 ②) ② and ② the ② step ② (② 3 ②) ② into ② a ② macroscopic ② traffic ② flow ② simulation ② module ② for ② simulation ②, ② wherein ② the ② macroscopic ② traffic ② flow ② simulation ② module ② has ② the ② following ② functions ② of ② customizing ② simulation ② duration ②, ② customizing ② traffic ② requirements ② including ② fixed ② requirements ② and ② elastic ② requirements ②, ② customizing ② road ② resistance ② functions ②, ② customizing ② various ② different ② vehicle ② types ②, ② simulating ② traffic ② mode ② sharing ②, ② customizing ② a ② traffic ② control ② scheme ②, ② simulating ② the ② path ② selection ② behavior ② of ② vehicles ② on ② the ② road ② network ② according ② to ② a ② random ② user ② balance ② distribution ② principle ②, ② generating ② a ② report ② form ② after ② the ② simulation ② is ② finished ②, ② and ② outputting ② road ② network ② operation ② information ②, ② wherein ② the ② information ② includes ② but ② is ② not ② limited ② to ② the ② total ② traffic ② requirements ② of ② the ② road ② network ②, ② the ② number ② of ② people ② selecting ② each ② traffic ② mode ②, ② the ② flow ② of ② vehicles ② of ② different ② types ② on ② each ② road ② section ② in ② the ② road ② network ②, ② the ② total ② travel ② time ② of ② the ② road ② network ② and ② the ② discharge ② amount ② of ② pollutants ② of ② different ② types ② in ② the ② road ② network ②. ②
The macroscopic traffic flow simulation module in (4.2) can be commercial traffic simulation software which can meet the simulation requirements in (4.1) after secondary development, such as Vissim, Aimsun, TransModeller and the like, and can also be an autonomously established traffic flow simulation model such as a cellular transmission model, or a mathematical model represented in a mathematical programming form. The difference is that commercial traffic simulation software can be directly used after data is input, and the self-established model needs to be converted into an executable program by using a programming language and then input data for traffic flow simulation. The programming languages include, but are not limited to MATLAB, C, C + +, JAVA.
(4.3) the simulation of the macroscopic traffic flow is carried out according to the following steps:
(4.3.1) the number of calculations is set, n is 1.
And (4.3.2) determining the traffic demand under each traffic mode according to the input OD array and the traffic mode sharing model. The assumption made here is that the traveler uses the same transportation means from the start point to the end point, and there is no transition of the transportation means in the middle. The traffic mode sharing model adopts a non-centralized model taking an individual as a unit, and comprises but is not limited to a Logit model and a Probit model.
(4.3.3) for each specific traffic mode k, according to the average passenger capacity m, the traffic demand people number matrix QkConverting into a traffic demand vehicle number matrix qk. Conversion method is qk＝Qk/m。
(4.3.4) for each specific mode of transportation k and traffic demand qkCarrying out traffic distribution according to a random user balance model to obtain a road section flow matrix xk,n. The principle of allocation is: the generalized road resistance is the only factor for determining the path selection; the generalized road resistance is influenced by traffic flow and traffic control measures; different traffic modes do not influence each other. The random user balance model can simulate uncertainty of a traveler on road resistance estimation, and includes but is not limited to a multivariate Logit model, a multivariate Probit model and a Burrell model.
(4.3.5) judging whether the number of times n is greater than 3, if n is greater than 3, executing the step (4.3.6); otherwise, adding 1 to n, substituting the road section flow matrix in the step (4.3.4) according to a calculation formula of the generalized road resistance function, updating the generalized road resistance, executing the step (4.3.2), and starting new flow distribution.
(4.3.6) entering the step to show that the calculation times meet the requirement of judging the road network balance. For each specific traffic mode k, according to the formulaCalculating the average value of the flow distribution results of the n, n-1, n-2 timesAnd the average value of the flow distribution results of the (n-1, n-2, n-3) th timeAnd (6) comparing. If the difference value of the two exceeds the preset precision range, the road network is not balanced, n needs to be added by 1, and the steps are carried out according to the calculation formula of the generalized road resistance functionAnd (4.3.4) the road section flow matrix is substituted, the generalized road resistance is updated, and the step (4.3.2) is executed to start the flow distribution for the new time. And if the difference value of the two is within the preset precision range, judging that the road network has reached balance, and finishing the calculation.
(4.3.7) outputting the flow matrix x of each traffic mode kk,nTotal travel time of the road network, and the emission of different types of pollutants in the road network.
(5) Traffic control effectiveness evaluation
Comparing the result of the macroscopic traffic flow simulation with the expected control effect of the traffic manager in the step (1). The compared indexes have at least two items, one item reflects the overall operation efficiency of the road network, and the other item reflects the overall environmental benefit of the road network. If the comparison result does not reach the expected target, executing the step (6); if so, step (7) is performed.
(6) Traffic management and control scheme optimization
The control scheme of (6.1) comprises at least two traffic control measures. For each traffic control measure, a set of control variable expressions is used, and changes of specific values of the control variables reflect changes of traffic control schemes.
And (6.2) storing all the control variables by using a one-dimensional array to serve as a traffic control scheme, and optimizing the traffic control scheme by adopting an optimization algorithm to obtain a new traffic control scheme.
And (6.3) substituting a new traffic control scheme into the generalized road resistance function, updating the road resistance, executing the step (4) and performing a new macroscopic traffic flow simulation.
(7) A new traffic control scheme is issued through the vehicle-mounted terminal, so that travelers can plan travel modes and paths in advance.
Further, in the step (1), the control area is characterized by: the urban pedestrian crossing is located in an urban area, clear in regional boundary, complete in regional internal road section, at least comprises one signal control crossing, high in requirement on air quality and frequent in and out of pedestrians. Including but not limited to scenic spots, historic building gathering areas, residential areas, pedestrian streets, and the like.
Further, in the step (1), the road network operation efficiency may be represented by total travel time or total generalized travel cost of all travelers; the environmental benefit can be expressed by the indexes of the pollutant discharge amount of the motor vehicle, the pollutant concentration in the air, the air quality index and the like. The desired control effect may be that the actual value of each indicator reaches a desired value, e.g. the actual value of the concentration of pollutants in the air is lower than the desired value; it is also possible that the actual change in each index achieves a desired change, such as a ten percent reduction in vehicle pollutant emissions.
Further, the step (2) is specifically as follows:
and (2.1) obtaining road network information in the control area through commercial network map search services, such as Baidu maps, Gagde maps and the like. Including road geometry, number of lanes, length of road segments L, and adjacency relationships between road segments.
And (2.2) acquiring the traffic flow x of different traffic modes k on each road section a through a vehicle-mounted GPS in the Internet of vehicles and a roadside license plate recognition device.
And (2.3) obtaining the average emission of each pollutant in unit time of each vehicle through a vehicle-mounted emission detection device in the Internet of vehicles. Among the contaminants that can be measured are, but not limited to, carbon dioxide, carbon monoxide, particulates, hydrocarbons, and nitrogen oxides.
And (2.4) acquiring pollutant concentration information in the control area through an air quality detection station.
And (2.5) obtaining the traffic control scheme of each road section and intersection in the control area through a traffic control system.
Further, in the step (6), the traffic control scheme may be divided into two types, discrete and continuous, according to the characteristics of the control variables. The control variables of the discrete type traffic control scheme may use a 0-1 variable tableIf the road section and the steering are forbidden, the passing is 1, and the non-passing is 0; the control variable of the continuous type traffic control scheme is continuously changed within a set range, such as for road charging, may be represented using a road section charging rate p, where p ismax>＝p>＝pmin. For signal timing, the green-to-noise ratio λ representation of the road segment exit can be used, where λmax>＝λ>＝λmin。
Further, in the step (6.2), the optimization algorithm includes, but is not limited to, genetic algorithm, particle swarm algorithm, simulated annealing algorithm.
A multi-traffic control measure combined optimization system considering environmental benefits is provided, all vehicles in the system are connected to a network, vehicles and vehicles, vehicles and roads, vehicles and traffic control systems, and data and information can be transmitted. The system comprises a control range defining and expected effect setting module, a data acquisition module, a simulation data preprocessing module, a macroscopic traffic flow simulation module, a traffic control effect evaluation module, a control scheme optimization module and a control scheme issuing and implementing module.
The control range demarcation and expected effect setting module has the functions that a traffic manager inputs an area needing management and control measure optimization in the city range according to a control requirement, and determines a traffic evaluation index and an expected control effect of the area.
The data acquisition module has the functions of acquiring and storing roads, vehicles, environments and traffic control schemes, and is connected with the macroscopic traffic flow simulation module to realize data transmission and sharing.
The simulation data preprocessing module has the function of processing the data transmitted by the data acquisition module and converting the data into input parameters which can be directly used by the macroscopic traffic flow simulation module.
The macroscopic traffic flow simulation module has the functions of receiving the data provided by the simulation data preprocessing module, simulating the actual traffic flow, reproducing the actual traffic network operation condition and outputting the traffic network operation information to the traffic control effect evaluation module.
The traffic control effect evaluation module has the function of receiving traffic network operation information output by the macroscopic traffic flow simulation module, comparing the traffic network operation information with an expected target of a traffic manager and judging whether the current road network operation condition reaches the expected target or not. If the expected target is achieved, outputting the current control scheme to a control scheme issuing and implementing module; and if the expected target is not achieved, outputting the current control scheme to the control scheme optimization module for optimization.
The control scheme optimization module has the functions of receiving the control scheme which is output by the traffic control effect evaluation module and does not reach the expected control target, optimizing the scheme, outputting the optimized new scheme to the macroscopic traffic flow simulation module, and simulating again.
The control scheme issuing and implementing module has the functions of receiving the control scheme which is sent by the traffic control effect evaluation module and accords with the expected control target, externally disclosing the control scheme and applying the control scheme to actual traffic control.
The invention has the beneficial effects that: on the basis of fully utilizing the car networking data, the purpose of reducing the exhaust emission of motor vehicles in urban areas is achieved in a macroscopic view by a mode of jointly optimizing various traffic control measures. The data of the Internet of vehicles is fully utilized, so that time and energy consumed by manual acquisition and statistical and measurement errors are avoided, the accuracy is high, the real-time performance is good, and the data volume is large; macroscopic traffic flow simulation focuses on the overall operation effect of a road network from the perspective of a system, and is more suitable for large-area popularization and application compared with microscopic and mesoscopic simulation; on the same platform, joint optimization of various traffic control measures is realized, mutual influence among different control measures is fully considered, and actual operation requirements are better met; the method aims to reduce the exhaust emission of motor vehicles in urban areas, improves the air quality while ensuring the running efficiency of a road network, reduces the influence of traffic emission on the health of travelers, and improves the environmental benefit.
Drawings
FIG. 1 is a block diagram of a system for joint optimization of multiple traffic control measures in consideration of environmental benefits in an Internet of vehicles environment according to the present invention;
FIG. 2 is a road network topology graph analyzed by an example of the present invention;
fig. 3 is a macroscopic traffic flow simulation flow chart in the invention.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
The method is applied to a combined optimization system of various traffic control measures considering environmental benefits under the environment of internet of vehicles, all vehicles in the system are connected to a network, the vehicles and the vehicles, the vehicles and the roads, and the vehicles and the traffic control system, and data and information can be transmitted, as shown in figure 1. The system comprises a control range defining and expected effect setting module, a data acquisition module, a simulation data preprocessing module, a macroscopic traffic flow simulation module, a traffic control effect evaluation module, a control scheme optimization module and a control scheme issuing and implementing module. The control range demarcation and expected effect setting module has the function that a traffic manager inputs an area needing management and control measure optimization in the city range according to a control requirement and determines an expected control effect of the area. The data acquisition module has the functions of acquiring and storing roads, vehicles, environments and traffic control schemes, and is connected with the macroscopic traffic flow simulation module to realize data transmission and sharing. The simulation data preprocessing module has the function of processing the data transmitted by the data acquisition module and converting the data into input parameters which can be directly used by the macroscopic traffic flow simulation module. The macroscopic traffic flow simulation module has the functions of receiving the data provided by the simulation data preparation module, simulating the actual traffic flow, reproducing the actual traffic network operation condition and outputting the traffic network operation information to the traffic control effect evaluation module. The traffic control effect evaluation module has the function of receiving traffic network operation information output by the macroscopic traffic flow simulation module, comparing the traffic network operation information with an expected target of a traffic manager and judging whether the current road network operation condition reaches the expected target or not. If the expected target is achieved, outputting the current control scheme to a control scheme issuing and implementing module; and if the expected target is not achieved, outputting the current control scheme to the control scheme optimization module for optimization. The control scheme optimization module has the functions of receiving the control scheme which is output by the traffic control effect evaluation module and does not reach the expected control target, optimizing the scheme, outputting the optimized new scheme to the macroscopic traffic flow simulation module, and simulating again. The control scheme issuing and implementing module has the functions of receiving the control scheme which is sent by the traffic control effect evaluation module and accords with the expected control target, externally disclosing the control scheme and applying the control scheme to actual traffic control.
The method comprises the following steps:
and (1) determining the range and the expected control effect of the control area by the traffic manager.
(1.1) traffic managers define control areas to be optimized. Wherein the control area is characterized in that: the urban pedestrian crossing is located in an urban area, clear in regional boundary, complete in regional internal road section, at least comprises one signal control crossing, high in requirement on air quality and frequent in and out of pedestrians. Including but not limited to scenic spots, historic building gathering areas, residential areas, pedestrian streets, and the like. In this example, the defined control area is an area in shaoxing city district in Zhejiang province, where two signalized intersections are located. The area comprises a plurality of parks, has higher requirements on air quality and meets the control requirements.
(1.2) the traffic manager determines the traffic assessment index and the expected control effect. The traffic evaluation index has macroscopic characteristics, can reflect the overall operation condition of the road network, and gives consideration to the operation efficiency and the environmental benefits of the road network. The road network operation efficiency can be represented by total travel time or total generalized travel cost of all travelers; the environmental benefit can be expressed by the indexes of the pollutant discharge amount of the motor vehicle, the pollutant concentration in the air, the air quality index and the like. The desired control effect may be that the actual value of each indicator reaches a desired value, e.g. the actual value of the concentration of pollutants in the air is lower than the desired value; it is also possible that the actual change in each index achieves a desired change, such as a ten percent reduction in vehicle pollutant emissions. In this example, the selected evaluation criteria are the total travel time of all travelers in the road network and the total emissions of vehicles on the road network. The desired effect is to minimize total emissions without reducing road network operating efficiency.
And (2) obtaining information of roads, vehicles, environments, traffic control schemes and the like in the control area.
And (2.1) obtaining road network information in the control area through a commercial network map search service. Including road geometry, number of lanes, length of road segments L, and adjacency relationships between road segments. In this example, the control area network information is obtained from a Baidu map.
And (2.2) acquiring the traffic flow x of different vehicle types k on each road section a through a vehicle-mounted GPS and a roadside license plate recognition device in the Internet of vehicles.
And (2.3) obtaining the average emission amount of each pollutant in unit time of each vehicle through a vehicle-mounted emission device in the internet of vehicles. Among the contaminants that can be measured are, but not limited to, carbon dioxide, carbon monoxide, particulates, hydrocarbons, and nitrogen oxides.
And (2.4) acquiring pollutant concentration information in the control area through an air quality detection station.
And (2.5) obtaining the traffic control scheme of each road section and intersection in the control area through a traffic control system.
And (3) preprocessing macroscopic traffic flow simulation input data.
And (3.1) counting the total number of the road sections and the total number of the nodes in the control area defined in the step (1), numbering each road section and each node in sequence, and allocating a unique ID. The topology of the network in this example is shown in figure 2. Here, to simplify the study of the problem, only the one-way traffic flow is adopted as the analysis object. The circles in the road network represent nodes, the line segments represent road segments, letters on the nodes and numbers on the road segments respectively represent the numbers of the nodes, wherein the number of the nodes is 12, and the number of the road segments is 17. The adjacency matrix of the links is shown in table 1, and the length of the links is shown in table 2.
TABLE 1 road segment adjacency matrix
TABLE 2 road segment Length
| Road segment numbering | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| L(km) | 0.829 | 0.612 | 0.856 | 0.801 | 0.975 | 0.831 | 0.605 | 0.734 | 0.966 |
| Road segment numbering | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
| L(km) | 0.697 | 0.71 | 0.596 | 0.762 | 0.918 | 0.769 | 0.547 | 1.1 |
And (3.2) determining the type of the vehicle to be controlled in each traffic mode, and reversely deducing the traffic demand in the control area according to the traffic flow information obtained in the step (2) and the average passenger capacity of each vehicle. The traffic demand is expressed in the form of OD array, described by two-dimensional Q array, and for each element Q [ i ] [ j ] in the array, the mark i represents a starting point, the mark j represents a terminal point, the value of Q [ i ] [ j ] represents the traffic demand in unit time between i and j, and the traffic demand takes the number of people as a basic unit. In this example, there are only two traffic modes, and there is one vehicle type under each traffic mode, which is a car and a bus respectively. In addition, only OD pairs with node 1 as the starting point and node 12 as the end point were analyzed, and the traffic demand was 3000 people/hour.
And (3.3) deducing the traffic capacity C of each road section in the control area to vehicles of different vehicle types according to the road information and the traffic flow obtained in the step (2). In this example, the road traffic capacity of the car is C1The road traffic capacity of the bus is C2. For the road section without signal control, the traffic capacity is equal to the saturation flow rate S; for road segments with signal control, the saturation flow rate and the split green of the intersection associated with the road segment are jointly determined. Specific values for saturation flow rate are shown in the following table:
TABLE 3 road segment saturation flow Rate
| Road segment numbering | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| S1(veh/h) | 1800 | 1800 | 2000 | 1800 | 2100 | 1800 | 2100 | 2000 | 2100 |
| S2(veh/h) | 1200 | 1200 | 1334 | 1200 | 1400 | 1200 | 1400 | 1334 | 1400 |
| Road segment numbering | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
| S1(veh/h) | 2100 | 1800 | 1800 | 2000 | 1800 | 2100 | 1800 | 1800 | |
| S2(veh/h) | 1400 | 1200 | 1200 | 1334 | 1200 | 1400 | 1200 | 1200 |
And (3.4) acquiring the free flow speed v of the road section according to the road information. Estimating free flow time t of each road section for vehicles of different vehicle types according to the free flow speed v and the road section length L obtained in the step (2)0L/v. In this example, the free flow velocity is 60km/h, and the calculated free flow time on each link is shown in table 4:
TABLE 4 road segment free stream time
| Road segment numbering | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| t0(h) | 0.0138 | 0.0102 | 0.0143 | 0.0134 | 0.0163 | 0.0138 | 0.0101 | 0.0122 | 0.0161 |
| Road segment numbering | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
| t0(h) | 0.0116 | 0.0118 | 0.0099 | 0.0127 | 0.0153 | 0.0128 | 0.0091 | 0.0183 |
(3.5) according to the road section traffic capacity C and the free flow time t0And designing the time road resistance t of the road section to vehicles k of different vehicle types. Wherein t is a function of the flow x of the vehicle type k on the road section a, and the following conditions need to be satisfied: the function is monotonically increasing, continuously differentiable; when the flow is extremely small, the path resistance is close to zero flow impedance; allowing for the presence of a supersaturated flow. In this example, the BPR function is used as the time path resistance function.
And (3.6) determining the traffic control measures to be optimized and the specific road sections or intersections implemented by the traffic control measures, and recording the current traffic control scheme. The traffic control measures include but are not limited to road section and steering forbidding, signal timing, road charging, road section canalization, tide lane and traffic priority setting. In this example, the traffic control measures used are emission charges and signal timing. Wherein the charging for discharging is carried out by selecting the control variable as the charging rate p of the road section, and for two types of buses and cars, the charging mode is that the bus is not charged and the car is charged, and the charging rate on each road section is sequentially p1，p2，…p12Represents; when the signals are matched, the selected control variable is the green signal ratio lambda of the exit channel corresponding to the road section, the full red time and the yellow light time are ignored, and the green signal ratios of the exit channels of the road sections 7, 3, 12 and 8 are respectively set as lambda1，λ2，λ3，λ4。
And (3.7) determining the influence of the traffic control measures to be optimized on the time road resistance function, and adding the influence into the mathematical expression of the time road resistance function in the form of control variables to obtain a generalized road resistance function c. In this example, the generalized road resistance is expressed as:
c(x,p,λ)＝μt(x,λ)+lpef
where l is the length of the road section, p is the charging rate, efFor the emission factor, μ is the time value conversion coefficient, and t is the time path resistance, expressed as:
wherein alpha and β are model parameters, and generally, alpha is 0.15, β is 0.4. m, the average passenger capacity and S is the road section saturation flow rate;
and (3.8) determining the main pollutant type to be controlled according to the pollutant concentration information in the air measured in the step (2). The evaluation index of the pollutants can be the degree of harm of the pollutants to human health or the degree of influence of the pollutants on air visibility. In this case, the pollutant to be controlled is carbon dioxide, since it represents a large proportion of the pollutants emitted by the motor vehicle and causes a greenhouse effect.
And (3.9) estimating the average emission factor of the pollutants to be controlled in the step (3.4) in the range of the control area of different vehicle types according to the average emission amount of the pollutants in each vehicle unit time obtained in the step (2). In this example, the carbon dioxide emission factor for a car is 322.3g/km and the carbon dioxide emission factor for a bus is 1072.8 g/km.
Step (4) macroscopic traffic flow simulation
① and ① (① 4.1 ①) ① inputting ① the ① data ① obtained ① in ① the ① step ① (① 2 ①) ① and ① the ① step ① (① 3 ①) ① into ① a ① macroscopic ① traffic ① flow ① simulation ① module ① for ① simulation ①, ① wherein ① the ① macroscopic ① traffic ① flow ① simulation ① module ① has ① the ① following ① functions ① of ① customizing ① simulation ① duration ①, ① customizing ① traffic ① requirements ① including ① fixed ① requirements ① and ① elastic ① requirements ①, ① customizing ① road ① resistance ① functions ①, ① customizing ① vehicles ① of ① various ① different ① vehicle ① types ①, ① simulating ① traffic ① mode ① sharing ①, ① customizing ① traffic ① control ① schemes ①, ① simulating ① path ① selection ① behaviors ① of ① vehicles ① on ① a ① road ① network ① according ① to ① a ① random ① user ① balance ① distribution ① principle ①, ① generating ① a ① report ① form ① after ① simulation ① is ① finished ①, ① and ① outputting ① road ① network ① operation ① information ①, ① wherein ① the ① information ① includes ① but ① is ① not ① limited ① to ① total ① traffic ① requirements ① of ① the ① road ① network ①, ① the ① number ① of ① people ① taking ① vehicles ① of ① each ① type ①, ① the ① flow ① of ① vehicles ① of ① different ① types ① on ① each ① road ① section ① in ① the ① road ① network ①, ① the ① total ① travel ① time ① of ① the ① road ① network ① and ① the ① discharge ① amount ① of ① pollutants ① of ① different ① types ① in ① the ① road ① network ①. ①
The macroscopic traffic flow simulation module in (4.2) can be commercial traffic simulation software which can meet the simulation requirements in (4.1) after secondary development, such as Vissim, Aimsun, TransModeller and the like, and can also be an autonomously established traffic flow simulation model such as a cellular transmission model, or a mathematical model represented in a mathematical programming form. The difference is that commercial traffic simulation software can be directly used after data is input, and the self-established model needs to be converted into an executable program by using a programming language and then input data for traffic flow simulation. The programming languages include, but are not limited to MATLAB, C, C + +, JAVA. The macroscopic traffic flow simulation model used in the example is a model expressed in a mathematical programming form, and the programming language is MATLAB, which is specifically expressed as:
wherein,the total number of people going out is OD to r-s; q. q.srsThe number of people who take the car between OD and r-s is shown;the number of people who travel by the bus between OD and r-s; theta is a model parameter, and 1 is taken in the example; x is the number ofaThe traffic of people/h for going out by a car on the road section a;the pedestrian volume is the pedestrian volume of the bus on the road section a, and the pedestrian/h; c. Ca(xa,pa) Is a generalized road resistance function of the car on the road section a;is a generalized road resistance function of the bus on the road section a;representing a path/segment association, if segment a is on the ith path connecting OD to r-sOtherwiseThe passenger flow rate of the car on the ith path connecting the OD pair r-s, people/h;the pedestrian volume is the pedestrian volume/h for the travel of the bus on the ith path connecting the OD pair r-s; gamma ray1，γ2The model parameters represent uncertainty of a traveler on road resistance estimation, and the larger the value is, the more accurate the estimation is, and the example is 1.
(4.3) the simulation of the macroscopic traffic flow is carried out according to the following steps:
(4.3.1) the number of calculations is set, n is 1.
And (4.3.2) determining the traffic demand under each traffic mode according to the input OD array and the traffic mode sharing model. The assumption made here is that the traveler uses the same transportation means from the start point to the end point, and there is no transition of the transportation means in the middle. The traffic mode sharing model adopts a non-centralized model taking an individual as a unit, and comprises but is not limited to a Logit model and a Probit model. The Logit model is used in this example.
(4.3.3) for each specific traffic mode k, according to the average passenger capacity m, the traffic demand people number matrix QkConverting into a traffic demand vehicle number matrix qk. Conversion method is qk＝Qk/m。
(4.3.4) for each specific mode of transportation k and traffic demand qkCarrying out traffic distribution according to a random user balance model to obtain a road section flow matrix xk,n. The principle of allocation is: the generalized road resistance is the only factor for determining the path selection; the generalized road resistance is influenced by the traffic flow and the control means; different traffic modes do not influence each other. The random user balance model can simulate uncertainty of a traveler on road resistance estimation, and includes but is not limited to a multivariate Logit model, a multivariate Probit model and a Burrell model. In this example, a multivariate Logit model is used.
(4.3.5) judging whether the number of times n is greater than 3, if n is greater than 3, executing the step (4.3.6); otherwise, adding 1 to n, substituting the road section flow in the step (4.3.4) according to a calculation formula of the generalized road resistance function, updating the generalized road resistance, executing the step (4.3.2), and starting new flow distribution.
(4.3.6) entering the step to show that the calculation times meet the requirement of judging the road network balance. For each specific traffic mode k, according to the formulaCalculating the average value of the flow distribution results of the n, n-1, n-2 timesAnd the average value of the flow distribution results of the (n-1, n-2, n-3) th timeAnd (6) comparing. And if the difference value of the two values exceeds the preset precision range, the road network is not balanced, n is added by 1, the road section flow in the step (4.3.4) is brought in according to a calculation formula of the generalized road resistance function, the generalized road resistance is updated, the step (4.3.2) is executed, and the flow distribution for the new time is started. And if the difference value of the two is within the preset error precision range, judging that the road network has reached the balance, and finishing the calculation.
(4.3.6) outputting the flow matrix x of each specific traffic mode kk,nTotal travel time of the road network, and the emission of different types of pollutants in the road network.
Step (5) evaluation of traffic control effect
And (5.1) comparing the result of the macroscopic traffic flow simulation in (4.1) with the expected control effect of the traffic manager in (1.2). The compared indexes have at least two items, one item reflects the overall operation efficiency of the road network, and the other item reflects the overall environmental benefit of the road network. In this example, the total travel time of the travelers in the road network and the total emissions of the motor vehicles in the road network are used.
(5.2) for the comparison result in (5.1), if the expected target is not reached, executing step (6); if so, step (7) is performed.
Step (6) traffic management and control scheme optimization
The control scheme of (6.1) comprises at least two control measures. For each management and control measure, a group of control variable expressions are used, and the change of the traffic management and control scheme is reflected by the change of the specific numerical values of the variables. Traffic management schemes can be classified into discrete and continuous types according to the characteristics of control variables. The control variable of the discrete type traffic control scheme can be represented by 0-1 variables, such as road sections and steering forbidding, wherein the traffic is 1 and the traffic is not 0; the control variable of the continuous type traffic control scheme is continuously changed within a set range, such as for road charging, may be represented using a road section charging rate p, where p ismax>＝p>＝pmin. For signal timing, the green-to-noise ratio λ representation of the road segment exit can be used, where λmax>＝λ>＝λmin. In this example, consider the split range [0.05,0.95 ]]The emission charge value range is [0,0.5 ]]The unit/kg of the total weight of the material,
and (6.2) storing all the controlled variables by using a one-dimensional array to serve as a traffic control scheme, and optimizing the traffic control scheme by adopting an optimization algorithm to obtain a new traffic control scheme. The optimization algorithm includes, but is not limited to, genetic algorithm, particle swarm algorithm, simulated annealing algorithm.
And (6.3) substituting the new control scheme into the generalized road resistance function, updating the road resistance, executing the step (4) and performing the new macroscopic traffic flow simulation.
Step (7) control scheme distribution and implementation
And (7.1) issuing a new traffic control scheme through the vehicle-mounted terminal, so that the traveler plans a travel mode and a travel path in advance. In this example, the traffic control scheme obtained by the final optimization is shown in table 5:
TABLE 5 traffic control scheme table
| p1 | p2 | p3 | p4 | p5 | p6 | p7 | p8 | p9 | p10 | p11 |
| 0.01 | 0.34 | 0.44 | 0.13 | 0.47 | 0.18 | 0 | 0.41 | 0 | 0 | 0.41 |
| p12 | p13 | p14 | p15 | p16 | p17 | λ1 | λ2 | λ3 | λ4 | |
| 0.31 | 0.28 | 0.12 | 0 | 0.09 | 0.45 | 0.41 | 0.59 | 0.35 | 0.65 |
The above examples are not intended to limit the present invention, and the present invention is not limited to the above embodiments, and the present invention is within the scope of the present invention as long as the requirements of the present invention are met.
Claims (6)
1. A method for jointly optimizing multiple traffic control measures in consideration of environmental benefits is characterized by comprising the following steps:
(1) a traffic manager determines the range of a control area, traffic evaluation indexes and an expected control effect; the traffic evaluation index can reflect the overall operation condition of the road network and give consideration to the operation efficiency and the environmental benefit of the road network;
(2) acquiring information of roads, vehicles, environments and traffic control schemes in a control area;
(3) preprocessing macroscopic traffic flow simulation input data;
(3.1) counting the total number of the road sections and the total number of the nodes in the control area defined in the step (1), and numbering each road section and each node in sequence;
(3.2) determining the type of the vehicle needing to be controlled in each traffic mode, and reversely deducing the traffic demand in the control area according to the traffic flow information obtained in the step (2), wherein the traffic demand takes the number of people as a basic unit;
(3.3) deducing the traffic capacity C of each road section in the control area under different traffic modes according to the road information and the traffic flow information obtained in the step (2);
(3.4) obtaining the free flow speed v of the road section according to the road information; estimating free flow time t of each road section in different transportation modes according to the free flow speed v and the road section length L obtained in the step (2)0＝L/v；
(3.5) according to the road section traffic capacity C and the free flow time t0Designing time road resistance t of the road section under different traffic modes k; wherein t is a function of the traffic flow x of the traffic mode k on the road section a, and the function satisfies the following condition: monotonically increasing and continuously differentiable; when the flow is extremely small, the path resistance is close to zero flow impedance; allowing for the presence of a supersaturated flow;
(3.6) determining the traffic control measures to be optimized and the specific road sections or intersections implemented by the traffic control measures, and recording the current control scheme;
(3.7) determining the influence of the traffic control measures to be optimized on the time road resistance function, and adding the influence into the mathematical expression of the time road resistance function in the form of control variables to obtain a generalized road resistance function c;
(3.8) determining the type of pollutant to be controlled according to the information of the concentration of the pollutant in the air measured in the step (2);
(3.9) for the pollutants needing to be controlled in the step (3.8), estimating average emission factors of the pollutants in the range of the control area in different transportation modes according to the average emission amount of the pollutants in each vehicle unit time obtained in the step (2);
(4) the macroscopic traffic flow simulation specifically comprises the following substeps:
(4.1) setting the number of times of calculation, wherein n is 1;
(4.2) determining traffic demands under each traffic mode according to the input OD array and a traffic mode sharing model; the assumption made here is that the travelers use the same traffic mode from the starting point to the end point, and there is no conversion of the traffic mode in the middle; the traffic mode sharing model adopts a non-collective model taking an individual as a unit;
(4.3) for each specific traffic mode k, according to the average passenger capacity m, the traffic demand people number matrix QkConverting into a traffic demand vehicle number matrix qk(ii) a Conversion method is qk＝Qk/m；
(4.4) for each specific mode of transportation k and traffic demand qkCarrying out traffic distribution according to a random user balance model to obtain a road section flow matrix xk,n(ii) a The principle of allocation is: the generalized road resistance is the only factor for determining the path selection; the generalized road resistance is influenced by traffic flow and traffic control measures; different traffic modes do not influence each other;
(4.5) judging whether the number of times n is greater than 3, if n is greater than 3, executing the step (4.6); otherwise, adding 1 to n, substituting the road section flow matrix in the step (4.4) according to a calculation formula of the generalized road resistance function, updating the generalized road resistance, executing the step (4.2), and starting new flow distribution;
(4.6) for each specific traffic pattern k, according to the formulaCalculating the average value of the flow distribution results of the n, n-1, n-2 timesAnd the average value of the flow distribution results of the (n-1, n-2, n-3) th timeComparing; if the difference value of the two is beyond the preset precision range, the road network is not balanced, n needs to be added by 1, and the road section flow matrix in the step (4.4) is brought according to the calculation formula of the generalized road resistance functionUpdating the generalized road resistance, executing the step (4.2), and starting new flow distribution; if the difference value of the two is within the preset precision range, judging that the road network is balanced, and finishing the calculation;
(4.7) outputting the flow matrix x of each traffic mode kk,nTotal travel time of the road network, and the discharge amount of different types of pollutants in the road network;
(5) traffic control effectiveness evaluation
Comparing the result of the macroscopic traffic flow simulation with the expected control effect of the traffic manager in the step (1); the compared indexes have at least two items, one item reflects the overall operation efficiency of the road network, and the other item reflects the overall environmental benefit of the road network; if the comparison result does not reach the expected target, executing the step (6); if so, executing the step (7);
(6) traffic management and control scheme optimization
(6.1) the control scheme at least comprises two traffic control measures; for each traffic control measure, a group of control variable expressions are used, and the change of the specific numerical value of the control variable reflects the change of the traffic control scheme;
(6.2) storing all control variables by using a one-dimensional array to serve as a traffic control scheme, and optimizing the traffic control scheme by adopting an optimization algorithm to obtain a new traffic control scheme;
(6.3) substituting a new traffic control scheme into a generalized road resistance function, updating road resistance, executing the step (4), and performing a new macroscopic traffic flow simulation;
(7) a new traffic control scheme is issued through the vehicle-mounted terminal, so that travelers can plan travel modes and paths in advance.
2. The method for jointly optimizing multiple traffic control measures according to claim 1, wherein in the step (1), the control area is characterized by: the urban pedestrian crossing is located in an urban area, clear in regional boundary, complete in regional internal road section, at least comprises one signal control crossing, high in requirement on air quality and frequent in and out of pedestrians.
3. The method for jointly optimizing multiple traffic control measures according to claim 1, wherein in step (1), the road network operation efficiency is expressed by total travel time or total generalized travel cost of all travelers; the environmental benefit is expressed by the pollutant discharge amount of the motor vehicle, the pollutant concentration in the air and the air quality index; the expected control effect may be that the actual value of each index reaches a desired value, or that the actual change of each index reaches a desired change.
4. The method for jointly optimizing multiple traffic control measures by considering environmental benefits according to claim 1, wherein the step (2) is specifically as follows:
(2.1) obtaining road network information in the control area through a commercial network map search service; the method comprises the following steps of (1) including the geometric shape of a road, the number of lanes, the length L of road sections and the adjacency relation among the road sections;
(2.2) acquiring traffic flow x of different traffic modes k on each road section a through a vehicle-mounted GPS and a roadside license plate recognition device in the Internet of vehicles;
(2.3) obtaining the average emission of each pollutant in unit time of each vehicle through a vehicle-mounted emission detection device in the Internet of vehicles;
(2.4) acquiring pollutant concentration information in the control area through an air quality detection station;
and (2.5) obtaining the traffic control scheme of each road section and intersection in the control area through a traffic control system.
5. The method for jointly optimizing multiple traffic control measures according to claim 1, wherein in the step (6), the traffic control schemes are divided into discrete types and continuous types according to the characteristics of the control variables; the control variables of the discrete type traffic control scheme are expressed by using 0-1 variables; the control variables of the continuous type traffic control scheme are continuously changed within a set range.
6. The method for jointly optimizing multiple traffic control measures according to claim 1, wherein in the step (6.2), the optimization algorithm is a genetic algorithm, a particle swarm algorithm or a simulated annealing algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610310512.7A CN105894814B (en) | 2016-05-11 | 2016-05-11 | Consider a variety of traffic control measure combined optimization methods and system of environmental benefit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610310512.7A CN105894814B (en) | 2016-05-11 | 2016-05-11 | Consider a variety of traffic control measure combined optimization methods and system of environmental benefit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105894814A CN105894814A (en) | 2016-08-24 |
| CN105894814B true CN105894814B (en) | 2018-05-08 |
Family
ID=56702746
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610310512.7A Expired - Fee Related CN105894814B (en) | 2016-05-11 | 2016-05-11 | Consider a variety of traffic control measure combined optimization methods and system of environmental benefit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105894814B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107038863B (en) * | 2017-05-17 | 2020-02-18 | 东南大学 | Urban road network generalized road right calculation method considering comprehensive traffic management measures |
| CN107146411A (en) * | 2017-06-06 | 2017-09-08 | 成都信息工程大学 | Quantum behavior particle group optimizing method based on square Potential Trough Model |
| CN107977914B (en) * | 2017-11-14 | 2020-01-24 | 东南大学 | Urban traffic management and control strategy visual quantitative test method |
| CN109035769A (en) * | 2018-07-25 | 2018-12-18 | 深圳智达机械技术有限公司 | A kind of information management system |
| CN109830104B (en) * | 2019-03-06 | 2021-06-29 | 中南大学 | Intersection control efficiency analysis method based on macroscopic basic diagram |
| CN110164153A (en) * | 2019-05-30 | 2019-08-23 | 哈尔滨理工大学 | A kind of adaptive timing method of traffic signals |
| CN112687097A (en) * | 2020-11-16 | 2021-04-20 | 招商新智科技有限公司 | Highway highway section level data center platform system |
| CN113283809B (en) * | 2021-06-29 | 2024-02-20 | 重庆交通大学 | Logistics monitoring system for rural industry |
| CN114417614B (en) * | 2022-01-20 | 2023-10-27 | 悉地（苏州）勘察设计顾问有限公司 | Carbon emission reduction measuring and calculating method under central urban motor vehicle space management and control measure |
| CN116504076A (en) * | 2023-06-19 | 2023-07-28 | 贵州宏信达高新科技有限责任公司 | Expressway traffic flow prediction method based on ETC portal data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102004002808A1 (en) * | 2003-03-07 | 2004-09-30 | Volkswagen Ag | Road traffic control system for controlling traffic lights incorporates data collected from observation vehicles, that is floating car data, to improve the control of traffic lights |
| CN104575036A (en) * | 2015-01-28 | 2015-04-29 | 重庆云途交通科技有限公司 | Regional signal control method based on dynamic OD flow prediction and simulating optimization |
| CN103593986B (en) * | 2013-11-25 | 2015-12-02 | 东南大学 | A kind of main line green wave coordination control signal time method optimizing exhaust emissions |
| CN105206070A (en) * | 2015-08-14 | 2015-12-30 | 公安部交通管理科学研究所 | Real-time road traffic signal coordination optimization control method and control system thereof |
| CN105243855A (en) * | 2015-09-28 | 2016-01-13 | 大连理工大学 | Crossing signal timing optimization method for reducing exhaust gas emission of motor vehicle |
| CN105390000A (en) * | 2015-12-18 | 2016-03-09 | 天津通翔智能交通系统有限公司 | Traffic signal control system and method based on road condition traffic big data |
-
2016
- 2016-05-11 CN CN201610310512.7A patent/CN105894814B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102004002808A1 (en) * | 2003-03-07 | 2004-09-30 | Volkswagen Ag | Road traffic control system for controlling traffic lights incorporates data collected from observation vehicles, that is floating car data, to improve the control of traffic lights |
| CN103593986B (en) * | 2013-11-25 | 2015-12-02 | 东南大学 | A kind of main line green wave coordination control signal time method optimizing exhaust emissions |
| CN104575036A (en) * | 2015-01-28 | 2015-04-29 | 重庆云途交通科技有限公司 | Regional signal control method based on dynamic OD flow prediction and simulating optimization |
| CN105206070A (en) * | 2015-08-14 | 2015-12-30 | 公安部交通管理科学研究所 | Real-time road traffic signal coordination optimization control method and control system thereof |
| CN105243855A (en) * | 2015-09-28 | 2016-01-13 | 大连理工大学 | Crossing signal timing optimization method for reducing exhaust gas emission of motor vehicle |
| CN105390000A (en) * | 2015-12-18 | 2016-03-09 | 天津通翔智能交通系统有限公司 | Traffic signal control system and method based on road condition traffic big data |
Non-Patent Citations (2)
| Title |
|---|
| Low Emissions and Delay Optimization for an Isolated Signalized Intersection Based on Vehicular Trajectories;Ciyun Lin等;《PLOS ONE》;20151231;第1-17页 * |
| Optimization Model for Traffic Signal Control with Environmental Objectives;Shenpei Zhou等;《Fourth International Conference on Natural Computation》;20081231;第530-534页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105894814A (en) | 2016-08-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105894814B (en) | Consider a variety of traffic control measure combined optimization methods and system of environmental benefit | |
| Pouresmaeili et al. | Development of Mashhad driving cycle for passenger car to model vehicle exhaust emissions calibrated using on-board measurements | |
| Yang et al. | Eco-driving system for connected automated vehicles: Multi-objective trajectory optimization | |
| CN102509470B (en) | System and method for realizing energy conservation and emission reduction of vehicle based on dynamic path planning | |
| Zhang et al. | High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants in a traffic-populated eastern Asian city | |
| CN102496079B (en) | Monitoring method for energy consumption and emission on roads | |
| CN111696369B (en) | All-market road time-sharing and vehicle-division type traffic flow prediction method based on multi-source geographic space big data | |
| CN108198425A (en) | A kind of construction method of Electric Vehicles Driving Cycle | |
| CN104112357B (en) | A kind of urgent emergency planning method of city area-traffic of severe haze weather | |
| CN108871362A (en) | A kind of environmentally friendly trip route planing method of automobile dynamic | |
| CN106530692A (en) | Motor vehicle fuel consumption emission intensity calculating method based on traffic jam evaluation index | |
| Alshayeb et al. | Investigating impacts of various operational conditions on fuel consumption and stop penalty at signalized intersections | |
| CN104091440B (en) | Based on the urban traffic blocking Cost Estimation of category of roads and type of vehicle | |
| CN107240264A (en) | A kind of non-effective driving trace recognition methods of vehicle and urban road facility planing method | |
| CN110633558A (en) | Urban traffic system modeling system | |
| CN110738367A (en) | traffic exhaust emission prediction method based on deep residual error network | |
| CN112967493A (en) | Neural network-based prediction method for vehicle passing intersection travel time | |
| CN112767686B (en) | Road network automobile emission estimation method based on multi-source data fusion | |
| CN111126878B (en) | Urban traffic operation evaluation method based on ecological index | |
| CN110879862B (en) | Vehicle Information Query System Based on Internet of Things | |
| CN105046958A (en) | Highway traffic information acquisition node nonequidistance optimized layout method | |
| Zheng et al. | Study of multi-objective path planning method for vehicles | |
| Yao et al. | VSP‐based emission factor calibration and signal timing optimisation for arterial streets | |
| CN111127076A (en) | Pricing method in taxi sharing mode | |
| Wu et al. | Urban traffic signal timing optimization by reducing vehicle emissions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180508 Termination date: 20200511 |