CN115019507A - Real-time estimation method for urban road network travel time reliability - Google Patents
Real-time estimation method for urban road network travel time reliability Download PDFInfo
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Abstract
The invention discloses a real-time estimation method for urban road network travel time reliability, which comprises the following steps: collecting cross section traffic detection data in real time, and calculating delay time and travel time of each road section; calculating the delay travel time ratio of the whole road section according to the delay time and the travel time of each road section; setting a delay travel time ratio threshold according to city scale, road network structure, peak time period or investigation; and calculating the travel time reliability according to the delayed travel time ratio threshold. The invention provides a method for determining a delay travel time ratio threshold value based on traffic section detection data and a set of real-time estimation method of vehicle delay travel time ratio probability distribution expectation and variance, which reduce the constraint conditions of data volume and data types, are more suitable for urban road networks and have wider application range; the influence of intersection signal control in the urban road network on the reliability estimation of the road network is considered, and the accuracy of the reliability estimation of the road network is improved.
Description
Technical Field
The invention relates to the technical field of traffic management, in particular to a real-time estimation method for the travel time reliability of an urban road network.
Background
The traffic network is a key system for stable operation of a super-large city. However, urban transportation networks are often subject to periodic or random disturbances, resulting in traffic problems such as blocked supply chains, increased individual travel costs, and the like. Traffic network reliability, which is a probabilistic expression of system risk, is defined as the probability that a traffic network provides a satisfactory level of service under random disturbances. The travel time reliability is an important index for measuring the reliability of the road network, and can be represented by the travel time distribution of the vehicle. The travel time reliability of the traffic network is estimated in real time, and the dynamic control and management optimization of the traffic network can be supported, so that the service level of the urban traffic network is further improved.
The measurement of the travel time reliability is mainly divided into two types, namely a mathematical analysis method and a statistical measurement method: the mathematical analysis method is based on traffic distribution models such as user balance and the like to calculate a result; the statistical measurement rule quantifies the reliability by measuring the average and standard deviation of the road network travel time. The mathematical analysis method considers a plurality of influences of random traffic process, traffic demand change, signal control scheme and the like, has wide applicability, but has complex modeling process and difficult parameter calibration. In recent years, the continuous improvement of traffic data acquisition hardware facilities enables increasingly abundant traffic detection data to be used for estimating travel time reliability, and a statistical measurement method is newly developed.
The method can solve the problems existing in the current travel time reliability estimation based on the traffic detection technology, such as increasing OD data signal control data input to improve the reliability estimation precision, simplifying a road network model to improve the reliability estimation calculation efficiency and the like. However, the existing statistical measurement method still mostly adopts traffic history data as input conditions, and cannot well analyze the dynamic change of travel time reliability by using real-time traffic data. Therefore, the method for estimating the reliability of the travel time in real time is found, and has important significance and value for supporting the control and management of the dynamic road network.
A method for calculating the reliability of the travel time of highway based on delay time coefficient (CN106960572B) features that based on the traffic data detection of highway, the information is collected, processed and released to monitor and release the reliability of travel time of highway, and the vehicles on highway are induced and controlled. Although the method can also estimate the travel time reliability of the traffic network in real time, the method has the following defects:
(1) the application scene is an expressway, the applied data detection means are radar detectors and bayonet detectors, the detection means are not completely applied to an urban road network, and detection equipment of urban road network equipment is not fully covered;
(2) influence of intersection signal control in the urban road network on road network reliability estimation is not considered, and accuracy of road network reliability estimation is greatly influenced.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides a method for estimating the reliability of the travel time of the urban road network in real time, and the reliability of the travel time of the traffic road network is estimated in real time by using a section traffic detector.
The technical scheme of the invention is as follows:
the real-time estimation method for the travel time reliability of the urban road network comprises the following steps:
s1, defining all road segments as a set U, wherein the set U comprises n road segments; collecting data of the cross-section traffic detector in real time to obtain traffic volume x of road section u u Wherein: u-a, b, c … n; u belongs to U;
s2, calculating the delay time d of the road section u u (x u ) And time of flight t u (x u )；
S3, repeating the step S2 until the delay time and the travel time of each road section in the set U are calculated;
s4, calculating the delay travel time ratio of all the road sections:
s5, the ratio of delay travel time obeys normal distribution X-N (mu, sigma) 2 ) The expected value μ of the normal distribution is the delay travel time ratio of step S4; calculating the variance σ of the delayed travel time ratio 2 ；
S6, selecting the probability distribution percentile omega of the delay travel time ratio according to the city scale, the road network structure, the peak time period or the investigation, and calculating the threshold pc of the delay travel time ratio according to the probability distribution percentile omega 0 The method comprises the following steps:
due to the total X to N (mu, sigma) 2 ) For total X and a given ω (0 < ω ≦ 1), if X is present ω So that P { X ≧ X ω ω, then pc 0 ＝x ω ；
S7, according to the delay travel time ratio threshold value pc 0 Calculating the travel time reliability of the road network during dt:
wherein: r represents the travel time reliability of the road network, P represents the probability that the delay travel time ratio of all vehicles in the road network is smaller than the reliability threshold, x represents the delay travel time ratio, mu represents the expected value of the delay travel time ratio, sigma 2 Representing the variance of the ratio of the delay times.
Further, in step S2, the travel time t u (x u ) The calculation formula of (a) is as follows:
delay time d u (x u ) The calculation formula of (c) is as follows:
wherein:
representing the free path time, x, of the road section u u Traffic volume, C, representing road section u u Representing the capacity of the road section u, alpha, beta are given parameters.
Further, the variance σ of the delayed stroke-time ratio is calculated in step S5 2 The steps are as follows:
s51, calculating total travel time TTS and total travel distance TTD of the vehicle according to the section traffic detection data; the total vehicle travel time TTS refers to the total time of all vehicles running in a road network, and the total vehicle travel distance TTD refers to the total distance of all vehicles running in a certain time;
s52, calculating the variance sigma of the delay travel time ratio according to the total travel time TTS and the total travel distance TTD of the vehicle 2 。
Further, in step S51, the calculation formula of the total vehicle travel time TTS is as follows:
the calculation formula of the total vehicle travel distance TTD is as follows:
wherein: t represents the total time range of reliability analysis, and is the sum of all time windows;
represents the total number of vehicles traveling on the section a during dt;
representing the flow of segment a during dt; l is a Representing the length of the road segment a.
Further, the variance σ of the delay time-of-flight ratio in step S52 2 The calculation formula of (a) is as follows:
where δ is the adjustment factor.
The principle of the invention is as follows:
a traveler wishes to arrive at a destination at an expected time, and reducing the traveler's travel time fluctuations provides a higher benefit to the traveler than reducing the traveler's expected travel time. The trip time of the traveler mainly comprises the running time of free flow and delay, and the delay fluctuation can cause the trip time of the traveler to be obviously changed. The invention defines that the traveler finishes the purpose of one trip, and when the ratio of delay to travel time is less than or equal to a certain given value, the trip of the traveler is reliable; when the ratio of the delay to the travel time is greater than a given value, the trip of the traveler is unreliable.
System reliability refers to the ability (or probability) of a system to perform a specified function within a predetermined time. For a real-time road network system, in any time window, according to the ratio of delay to travel time (delay travel time ratio) of all vehicles leaving the road network, a cumulative probability distribution curve of the delay to travel time ratio can be obtained.
At a given delayed travel time ratio threshold pc 0 In the case of (2), the probability of the travel reliability of the vehicle leaving the road network in the time zone can be calculated, and the index is taken as the travel time reliability of the road network in the time zone, as shown in equation (1):
wherein: r represents the travel time reliability of the road network, F represents the probability of the primary travel reliability of all vehicles in the road network, P represents the probability that the delay travel time ratio of all vehicles in the road network is smaller than the reliability threshold, N represents the number of vehicles with reliable travel in the total number of vehicles leaving the road network within t time, and N represents the total number of vehicles leaving the road network within certain time.
The formula (1) represents: the travel time reliability is numerically equal to the probability that the ratio of the delayed travel times of all the vehicles in the road network is smaller than the threshold. The larger the quantitative index of the travel time reliability is, the larger the probability that the actual travel time is equal to or less than the expected travel time is, the smaller the fluctuation of the vehicle travel time in the road network is, and the higher the service level of the road network is. Wherein the delay travel time is compared with a threshold value pc 0 And determining according to the distribution characteristics of the ratio of the vehicle delay to the travel time in the road network. In the formula (1), the larger r (t), the higher the ratio of the number of vehicles having a reliable route in the road network, and the more reliable the road network.
Since the delayed travel time ratio follows a normal distribution: d/t ═ X to N (mu, sigma) 2 ) Therefore, the travel time reliability of the road network during dt is:
when estimating the travel time reliability, it is necessary to determine the delay travel time ratio threshold of the road network. And determining the percentage of the probability distribution of the delay travel time ratio to the threshold reference delay travel time ratio:
due to the total X to N (mu, sigma) 2 ) For total X and a given ω (0 < ω ≦ 1), if X is present ω So that P { X ≧ X ω ω, then pc 0 ＝x ω . The selection of omega is based on city scale, road network structure, peak time period, etc. and may be selected based on survey.
The expected value of the delay travel time ratio has a correlation with the density of the road network vehicles. The invention refers to a basic function of researching road section impedance, namely a business of Public road Bureau (BPR) function, for analyzing the travel time of a road section, and the specific form is as follows:
within dt, the delay for segment a is:
dividing the formula (4) by the formula (3) to obtain the delay travel time ratio of the vehicle on the road section a, so that the delay travel time ratio of the whole road network u in a certain time period is as follows:
u＝a,b,c…n；u∈U
assuming that the traffic flow in the road network u is constant in the period dt, calculating the delay travel time ratio expected value mu of the road network vehicle in real time according to the formula (5).
The estimation of the variance of the delay travel time ratio of the vehicles in the road network is the key point and the difficulty of the invention. When the saturation of each road segment in the road network is uniform, the total travel distance of the vehicles in the road network has a fixed relation with the total number of the vehicles (namely, a normal curve, as shown by a solid line in fig. 1). According to the relation between the total driving distance of vehicles and the total number of vehicles, the traffic flow of the road network can be divided into three states: free flow, critical flow, congested flow. Matching the road network traffic flow state with the road network reliability degree, wherein the corresponding relation is as follows: free flow-ideal, critical flow-reliable, crowded flow-less reliable.
When the road network is affected by an emergency such as a traffic accident, a point formed by the total travel distance of the vehicles and the total number of the vehicles is below a normal curve (as shown by a dotted line below fig. 1). The present invention assumes: when the density of the vehicles in the road network is unchanged and the expectation is unchanged, the more upward deviation of the scattered points indicates that the total driving time of the vehicles in the road network is the same, the larger the total driving distance of the vehicles is, the higher the reliability of the road network is, and the smaller the variance is; and if the scatter point deviates downwards, the reliability of the road network is lower, the variance is larger, and a variance estimation method is established according to the variance.
The estimation of a normal curve of the total driving distance and the total vehicle number of the vehicles in the road network and the estimation of actual relation point positions at all times can be realized based on the section traffic detection data. The real-Time estimation method of the Total Travel Distance (Total Distance traveled by the vehicle in a certain Time, namely TTD) and the Total Travel Time (numerically equivalent to the Total number of vehicles, Total Time traveled by the vehicle in a road network, Total Time Spent, namely TTS) of the vehicle is as follows:
the invention constructs a linear model to estimate the variance sigma of the delay travel time ratio 2 The real-time estimation method is shown as follows:
the beneficial technical effects of the invention are as follows:
(1) the method for determining the delay travel time ratio threshold based on traffic section detection data and a set of real-time estimation methods of vehicle delay travel time ratio probability distribution expectation and variance are provided, so that the constraint conditions of data quantity and data types are reduced;
(2) different from the situation that the vehicle track data has low data permeability in a small city, the cross section traffic detector has wide layout coverage, and has certain applicability in a large city and a small county city, so that the invention has wider application range;
(3) the influence of intersection signal control in the urban road network on the reliability estimation of the road network is considered, and the accuracy of the reliability estimation of the road network is improved.
Drawings
Fig. 1 is a diagram of a total vehicle travel distance TTD versus a total vehicle travel time TTS;
FIG. 2 is a microscopic simulation road network of an embodiment;
FIG. 3 is a true trend plot of travel time reliability at different delayed travel time ratios thresholds;
FIG. 4 is a plot of true and estimated values of the time-of-flight reliability at a signal period of 60 s;
FIG. 5 is a plot of true versus estimated values of the time-of-flight reliability for a signal period of 90 s;
fig. 6 is a comparison graph of the true and estimated values of the travel time reliability at a signal period of 120 s.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and examples. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The example is a 3 × 3 standard square micro-simulation road network, as shown in fig. 2. And each inlet passage inputs the same fixed flow and ensures the road network traffic flow to be evenly distributed through a traffic distribution model. As the signal period can influence the operation efficiency and reliability of the traffic network, three scenes of 60s, 90s and 120s of the signal period of each intersection are selected, and an estimated value and a true value of the travel time reliability are calculated.
First, calculate the estimated value of the travel time reliability
The invention is adopted to calculate the estimated value of the travel time reliability, and the steps are as follows:
s1, defining all road segments as a set U, wherein the set U comprises n road segments; collecting data of the cross section traffic detector in real time to obtain the traffic x of the road section u u Wherein: u ═ a, b, c … n; u ∈ U.
S2, calculating the delay time d of the road section u u (x u ) And time of flight t u (x u ). Time of flight t u (x u ) The calculation formula of (a) is as follows:
delay time d u (x u ) The calculation formula of (a) is as follows:
wherein:
representing the free path time, x, of the road section u u Traffic volume, C, representing road section u u Representing the capacity of the road section u, alpha, beta are given parameters.
And S3, repeating the step S2 until the delay time and the travel time of each road section in the set U are calculated.
S4, calculating the delay travel time ratio of all the road sections:
s5, the ratio of delay travel time obeys normal distribution X-N (mu, sigma) 2 ) The expected value μ of the normal distribution is the delay time-of-flight ratio in step S4. Calculating the variance σ of the delayed travel time ratio 2 The method comprises the following specific steps:
and S51, calculating the total travel time TTS and the total travel distance TTD of the vehicle according to the section traffic detection data. The total vehicle travel time TTS is the total time of all vehicles traveling in the road network, and the total vehicle travel distance TTD is the total distance of all vehicles traveling in a certain time.
The calculation formula of the total travel time TTS of the vehicle is as follows:
the calculation formula of the total travel distance TTD of the vehicle is as follows:
wherein: t represents the total time range of reliability analysis, and is the sum of all time windows;
represents the total number of vehicles traveling on the section a during dt;
representing the flow of segment a during dt; l is a radical of an alcohol a Representing the length of the road segment a.
S52, calculating the variance sigma of the delay journey time ratio according to the total journey time TTS and the total driving distance TTD of the vehicle 2 ：
Where δ is the adjustment factor.
S6, selecting the probability distribution percentile omega of the delay travel time ratio according to the city scale, the road network structure, the peak time period or the investigation, and calculating the threshold pc of the delay travel time ratio according to the probability distribution percentile omega 0 The method comprises the following steps:
due to the total X to N (mu, sigma) 2 ) For total X and a given ω (0 < ω ≦ 1), if X is present ω So that P { X ≧ X ω ω, then pc 0 ＝x ω 。
In order to determine the delay travel time ratio threshold in the simulation calculation example, the distribution condition of the delay travel time ratio of the vehicles in the road network is analyzed based on the simulation data, and the road network delay travel time ratio threshold is set. The percentage of the probability distribution of the delay travel time ratio of the vehicles in the road network is shown in table 1.
TABLE 1 percentile probability distribution of delay travel time ratio of vehicles in road network
Fig. 3 is a true trend graph of travel time reliability of the road network under different delay travel time ratio thresholds. Therefore, under the condition of different delay travel time ratios, the change trends of the travel time reliability are consistent; the reliability of the travel time in the area encircled by the solid line frame decreases rapidly, reflecting that the reliability of the travel time will decrease rapidly when the total travel distance of the vehicle is within a certain range. From the above analysis, it can be known that the delay travel time ratio threshold corresponding to the 75% quantile can reflect the overall variation trend of the travel time reliability, and therefore, the embodiment selects the delay travel time ratio corresponding to the 75% quantile, that is, pc 0 0.6899 is used as the delay travel time ratio threshold of the road network.
S7, according to the delay travel time ratio threshold value pc 0 0.6899, calculating the travel time reliability of the road network during dt:
wherein: r represents the travel time reliability of the road network, P represents the probability that the delay travel time ratio of all vehicles in the road network is smaller than the reliability threshold, x represents the delay travel time ratio, mu represents the expected value of the delay travel time ratio, sigma 2 Representing the variance of the ratio of the delay times.
The estimated values of the reliability of the travel time obtained finally are shown as solid points in fig. 4 to 6.
Second, calculate the truth value of the reliability of the journey time
From the travel time and delay data of vehicles leaving the road network in each time window in the simulated vehicle data, a true value of the reliability of the travel time can be calculated by the formula of step S7, and the result is shown as the open dots in fig. 4 to 6.
Third, comparative analysis of estimated value and true value
As can be seen from FIGS. 4 to 6:
(1) fig. 4 shows that, at the initial stage of TTS increase, the road network traffic flow is in a free flow state, and at this time, the estimated value of the travel time reliability is consistent with the true value and is 1; when TTS is larger than a certain value, the traffic flow state of the road network gradually approaches to critical flow, the vehicles begin to have congestion, the estimated value and the true value both begin to decrease, the overall change trend is consistent, but the estimated value is larger than the true value, and the area with larger error is concentrated in the area A; when TTS continues to increase, the traffic flow of the road network gradually reaches a congestion state, at the moment, the road network vehicles are greatly delayed due to the congestion phenomenon, and the reliability of the travel time is rapidly reduced, which shows that the travel of the road network vehicles is unreliable at the moment, the ratio of the delayed travel time is too high, and the delay travel time exceeds the threshold value of the ratio of the delayed travel time.
(2) Fig. 5 shows that although the estimated value of the travel time reliability and the true value change trend in the same way, the estimated value is slightly smaller than the true value, and the region with larger error is concentrated in the B region.
(3) The same is true in fig. 6, and the phenomenon of small estimated values is more obvious, and the region with larger errors is concentrated in the region C.
In summary, the A, B, C area shows that the estimation method of the present invention has errors when the road network is close to saturation. And (3) carrying out error analysis on the true value and the estimated value of the travel time reliability, wherein the simulation time is 180 minutes each time, each statistical time window is 60 seconds, five different random seeds are taken in each experiment and averaged, and the error analysis result is shown in the following table 2.
TABLE 2 true and estimated values of travel time reliability
From the analysis results, the average absolute errors of the estimation method in the research are respectively 0.0568, 0.0617 and 0.0759 when the road network signal period is respectively 60s, 90s and 120s, and the relative error is less than 10%, which shows that the estimation method can be applied to engineering practice.
While the embodiments of the invention have been described in detail, it is not intended to limit the invention to the exact construction and operation illustrated and described, and it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made herein without departing from the spirit and scope of the invention.
Claims (5)
1. The real-time estimation method for the travel time reliability of the urban road network is characterized by comprising the following steps of:
s1, defining all road segments as a set U, wherein the set U comprises n road segments; collecting data of the cross section traffic detector in real time to obtain the traffic x of the road section u u Wherein: u ═ a, b, c … n; u belongs to U;
s2, calculating the delay time d of the road section u u (x u ) And time of flight t u (x u )；
S3, repeating the step S2 until the delay time and the travel time of each road section in the set U are calculated;
s4, calculating the delay travel time ratio of all the road sections:
s5, the ratio of delay travel time obeys normal distribution X-N (mu, sigma) 2 ) The expected value μ of the normal distribution is the delay travel time ratio of step S4; calculating the variance σ of the delayed travel time ratio 2 ；
S6, selecting the probability distribution percentile omega of the delay travel time ratio according to the city scale, the road network structure, the peak time period or the investigation, and calculating the threshold pc of the delay travel time ratio according to the probability distribution percentile omega 0 The method comprises the following steps:
due to the total X to N (mu, sigma) 2 ) For the total X and a given ω (0 ≦ ω ≦ 1), if X is present ω So that P { X ≧ X ω ω, then pc 0 ＝x ω ；
S7, according to the delay travel time ratio threshold value pc 0 Calculating the travel time reliability of the road network during dt:
wherein: r represents the travel time reliability of the road network, P represents the probability that the delay travel time ratio of all vehicles in the road network is smaller than the reliability threshold, x represents the delay travel time ratio, mu represents the expected value of the delay travel time ratio, sigma 2 Representing the variance of the delayed travel time ratio.
2. The urban road network travel time reliability real-time estimation method according to claim 1, characterized in that:
in step S2, the travel time t u (x u ) The calculation formula of (a) is as follows:
delay time d u (x u ) The calculation formula of (a) is as follows:
wherein:
representing the free path time, x, of the road section u u Traffic volume, C, representing road section u u Representing the capacity of the road section u, alpha, beta are given parameters.
3. The method according to claim 1, wherein the variance σ of the delay travel time ratio is calculated in step S5 2 The steps are as follows:
s51, calculating total travel time TTS and total travel distance TTD of the vehicle according to the section traffic detection data; the total vehicle travel time TTS refers to the total time of all vehicles running in a road network, and the total vehicle travel distance TTD refers to the total distance of all vehicles running in a certain time;
s52, calculating the variance sigma of the delay journey time ratio according to the total journey time TTS and the total driving distance TTD of the vehicle 2 。
4. The urban road network travel time reliability real-time estimation method according to claim 3, characterized in that:
in step S51, the calculation formula of the total vehicle travel time TTS is as follows:
the calculation formula of the total travel distance TTD of the vehicle is as follows:
wherein: t represents the total time range of reliability analysis, and is the sum of all time windows;
represents the total number of vehicles traveling on the section a during dt;
representing the flow of segment a during dt; l is a Representing the length of the road segment a.
5. According to the rightThe method for real-time estimation of travel time reliability of urban road network according to claim 3, wherein the variance σ of the delay travel time ratio in step S52 2 The calculation formula of (a) is as follows:
where δ is the adjustment factor.
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Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7657367B1 (en) * | 2006-11-16 | 2010-02-02 | Navteq North America, Llc | Method of developing traffic messages |
| CN101639871A (en) * | 2009-07-23 | 2010-02-03 | 上海理工大学 | Vehicle-borne dynamic traffic information induction system analog design method facing behavior research |
| KR20110116779A (en) * | 2010-04-20 | 2011-10-26 | 한국항공대학교산학협력단 | Agent-based travel time estimation apparatus and method thereof |
| CN102592447A (en) * | 2011-12-20 | 2012-07-18 | 浙江工业大学 | Method for judging road traffic state of regional road network based on fuzzy c means (FCM) |
| CN102737504A (en) * | 2012-07-13 | 2012-10-17 | 重庆大学 | Method for estimating bus arrival time in real time based on drive characteristics |
| CN103903433A (en) * | 2012-12-27 | 2014-07-02 | 中兴通讯股份有限公司 | Real-time dynamic judgment method and device for road traffic state |
| CN104821081A (en) * | 2015-04-22 | 2015-08-05 | 西安翔迅科技有限责任公司 | Road condition detection system based on vehicle GPS data |
| US20160003637A1 (en) * | 2013-11-01 | 2016-01-07 | Yahoo! Inc. | Route detection in a trip-oriented message data communications system |
| CN105590346A (en) * | 2016-02-18 | 2016-05-18 | 华南理工大学 | Tolling highway network traffic information acquisition and induction system based on path identification system |
| CN106600030A (en) * | 2016-11-02 | 2017-04-26 | 浙江大学 | Travel reserved time calculation method based on road network journey time reliability |
| US20170270790A1 (en) * | 2015-05-15 | 2017-09-21 | New York City Transit Authority | Vehicle Prediction Module |
| WO2018122585A1 (en) * | 2016-12-30 | 2018-07-05 | 同济大学 | Method for urban road traffic incident detecting based on floating-car data |
| CN108597235A (en) * | 2018-05-08 | 2018-09-28 | 中南大学 | Intersection signal parameter optimization and effect evaluation method based on traffic video data |
| US20180350237A1 (en) * | 2016-10-08 | 2018-12-06 | Dalian University Of Technology | Method for estimating distribution of urban road travel time in considering operation state of taxi |
| EP3437958A1 (en) * | 2017-08-03 | 2019-02-06 | Siemens Aktiengesellschaft | Estimation of expected journey times of a rail vehicle |
| CN109784546A (en) * | 2018-12-24 | 2019-05-21 | 上海海事大学 | Intersection workspace construction method based on Delay and cost control |
| US20190156662A1 (en) * | 2016-12-09 | 2019-05-23 | Dalian University Of Technology | A method for estimating road travel time based on built environment and low-frequency floating car data |
| CN110634292A (en) * | 2019-09-19 | 2019-12-31 | 北京航空航天大学 | Travel time reliability estimation method based on road resistance performance function |
| CN110634298A (en) * | 2019-10-17 | 2019-12-31 | 上海理工大学 | Estimation method for traffic efficiency reliability of intersection controlled by two-phase signals |
| CN110705056A (en) * | 2019-09-19 | 2020-01-17 | 北京航空航天大学合肥创新研究院 | Traffic distribution method considering perceived travel time reliability and late penalty |
| WO2021109318A1 (en) * | 2019-12-03 | 2021-06-10 | 东南大学 | Method for estimating and predicting short-term traffic circulation state of urban road network |
| CN113380027A (en) * | 2021-05-31 | 2021-09-10 | 中山大学 | Intersection traffic state parameter estimation method and system based on multi-source data |
| CN113420488A (en) * | 2021-05-18 | 2021-09-21 | 东南大学 | Urban road network OD estimation method based on track reconstruction |
| CN113887878A (en) * | 2021-09-09 | 2022-01-04 | 北京工业大学 | Traffic network reliability evaluation method based on entropy weight-TOPSIS method |
-
2022
- 2022-06-06 CN CN202210630005.7A patent/CN115019507B/en active Active
Patent Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7657367B1 (en) * | 2006-11-16 | 2010-02-02 | Navteq North America, Llc | Method of developing traffic messages |
| CN101639871A (en) * | 2009-07-23 | 2010-02-03 | 上海理工大学 | Vehicle-borne dynamic traffic information induction system analog design method facing behavior research |
| KR20110116779A (en) * | 2010-04-20 | 2011-10-26 | 한국항공대학교산학협력단 | Agent-based travel time estimation apparatus and method thereof |
| CN102592447A (en) * | 2011-12-20 | 2012-07-18 | 浙江工业大学 | Method for judging road traffic state of regional road network based on fuzzy c means (FCM) |
| CN102737504A (en) * | 2012-07-13 | 2012-10-17 | 重庆大学 | Method for estimating bus arrival time in real time based on drive characteristics |
| CN103903433A (en) * | 2012-12-27 | 2014-07-02 | 中兴通讯股份有限公司 | Real-time dynamic judgment method and device for road traffic state |
| US20160003637A1 (en) * | 2013-11-01 | 2016-01-07 | Yahoo! Inc. | Route detection in a trip-oriented message data communications system |
| CN104821081A (en) * | 2015-04-22 | 2015-08-05 | 西安翔迅科技有限责任公司 | Road condition detection system based on vehicle GPS data |
| US20170270790A1 (en) * | 2015-05-15 | 2017-09-21 | New York City Transit Authority | Vehicle Prediction Module |
| CN105590346A (en) * | 2016-02-18 | 2016-05-18 | 华南理工大学 | Tolling highway network traffic information acquisition and induction system based on path identification system |
| US20180350237A1 (en) * | 2016-10-08 | 2018-12-06 | Dalian University Of Technology | Method for estimating distribution of urban road travel time in considering operation state of taxi |
| CN106600030A (en) * | 2016-11-02 | 2017-04-26 | 浙江大学 | Travel reserved time calculation method based on road network journey time reliability |
| US20190156662A1 (en) * | 2016-12-09 | 2019-05-23 | Dalian University Of Technology | A method for estimating road travel time based on built environment and low-frequency floating car data |
| WO2018122585A1 (en) * | 2016-12-30 | 2018-07-05 | 同济大学 | Method for urban road traffic incident detecting based on floating-car data |
| EP3437958A1 (en) * | 2017-08-03 | 2019-02-06 | Siemens Aktiengesellschaft | Estimation of expected journey times of a rail vehicle |
| CN108597235A (en) * | 2018-05-08 | 2018-09-28 | 中南大学 | Intersection signal parameter optimization and effect evaluation method based on traffic video data |
| CN109784546A (en) * | 2018-12-24 | 2019-05-21 | 上海海事大学 | Intersection workspace construction method based on Delay and cost control |
| CN110634292A (en) * | 2019-09-19 | 2019-12-31 | 北京航空航天大学 | Travel time reliability estimation method based on road resistance performance function |
| CN110705056A (en) * | 2019-09-19 | 2020-01-17 | 北京航空航天大学合肥创新研究院 | Traffic distribution method considering perceived travel time reliability and late penalty |
| CN110634298A (en) * | 2019-10-17 | 2019-12-31 | 上海理工大学 | Estimation method for traffic efficiency reliability of intersection controlled by two-phase signals |
| WO2021109318A1 (en) * | 2019-12-03 | 2021-06-10 | 东南大学 | Method for estimating and predicting short-term traffic circulation state of urban road network |
| CN113420488A (en) * | 2021-05-18 | 2021-09-21 | 东南大学 | Urban road network OD estimation method based on track reconstruction |
| CN113380027A (en) * | 2021-05-31 | 2021-09-10 | 中山大学 | Intersection traffic state parameter estimation method and system based on multi-source data |
| CN113887878A (en) * | 2021-09-09 | 2022-01-04 | 北京工业大学 | Traffic network reliability evaluation method based on entropy weight-TOPSIS method |
Non-Patent Citations (4)
| Title |
|---|
| 何兆成;赵建明;王镇波;韦清波;: "城市信号控制路网中的路段行程时间估计方法", 交通运输工程学报, no. 04 * |
| 李昂;李硕;李玲;: "城市道路路段行程时间计算模型研究", 公路工程, no. 03 * |
| 王嘉文;杭佳宇;李思洁;周溪召;: "自适应信号控制交叉口行程时间可靠性", 系统工程, no. 05 * |
| 王志建;马超锋;李梁;: "低频GPS数据和交叉口延误下的行程时间估计", 西南交通大学学报, no. 02 * |
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