A tram intersection control strategy optimization method

Abstract

The present invention relates to a method for optimizing control strategies for tram intersections, comprising the following steps: analyzing factors influencing tram travel time; classifying tram travel time according to different scenarios corresponding to specific factors; based on tram lines and operation information, counting tram travel time in each classified scenario, and analyzing travel time reliability; constructing an optimal distribution model for tram travel time in each classified scenario; based on the optimal distribution model, calculating fitting parameters of travel time distribution in each classified scenario, analyzing the change law of travel time mean and travel time volatility; based on travel time reliability, travel time mean, travel time volatility and characteristics of actual tram route intervals, obtaining an optimized intersection signal control strategy. Compared with the prior art, the present invention can improve the reliability of modern tram travel time, and is also conducive to improving the punctuality rate of trams in different scenarios.

Description

Technical Field

[0001] The invention relates to the technical field of tram operation, and in particular to a tram intersection control strategy optimization method. Background Art

[0002] As a supplement to the urban public transportation system, modern trams have developed rapidly in recent years. Since modern trams share road rights with other types of traffic at intersections, traffic characteristics and signal control methods are usually key factors affecting the volatility of tram travel time. In order to improve the punctuality of modern trams, it is necessary to optimize the signal control method of modern trams at intersections based on the travel time reliability model.

[0003] At present, the control methods of modern trams at signalized intersections are generally based on optimization of predicted tram arrival time at intersections, comprehensive optimization with tram scheduling, and coordinated control of trunk signalized intersections based on speed guidance.

[0004] In summary, there is no research on optimizing the control of trams at signalized intersections based on the reliability of tram travel time. Summary of the invention

[0005] The purpose of the present invention is to provide a tram intersection control strategy optimization method in order to overcome the defects of the above-mentioned prior art. By establishing a model for tram travel time reliability analysis, and analyzing the dynamic and static factors affecting travel time reliability, based on statistical and probabilistic methods, the impact of different scenarios on tram travel time reliability is quantitatively analyzed, and a dynamic optimization strategy for intersection signal control is proposed in combination with the actual situation of the line, so as to improve the travel time reliability of modern trams and help improve the punctuality rate of trams in different scenarios.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A tram intersection control strategy optimization method comprises the following steps:

[0008] S1. Analyze the factors affecting tram travel time;

[0009] S2. Selecting a specific factor from the influencing factors, classifying the tram travel time according to different scenarios corresponding to the specific factor, and obtaining a plurality of classification scenarios;

[0010] S3. Based on the tram line and operation information, the travel time of the tram in each classification scenario is counted and the reliability of the travel time is analyzed;

[0011] S4, constructing the optimal distribution model of tram travel time under each classification scenario;

[0012] S5. Based on the optimal distribution model, calculate the fitting parameters of the travel time distribution under each classification scenario, and analyze the change rules of the travel time mean and travel time volatility;

[0013] S6. Based on the travel time reliability, travel time mean, travel time volatility and characteristics of the actual tram route section, an optimized intersection signal control strategy is obtained.

[0014] Furthermore, the specific factors include the number of signalized intersections between adjacent sites and traffic flow characteristics during peak and off-peak hours.

[0015] Furthermore, the method of calculating the travel time of trams in various classification scenarios based on tram lines and operation information includes the following steps:

[0016] Based on the actual time when the tram arrives at each station, calculate the actual travel time T between different ODs OD ;

[0017] Based on the actual driving time T OD , calculate the actual travel time t per unit mileage between different ODs;

[0018] t=T OD / L OD

[0019] Among them, L OD is the distance between the departure site and the destination site;

[0020] The sample data of the actual travel time t per unit mileage in each classification scenario are counted, and the sample data includes the sample quantity, sample mean and sample standard deviation.

[0021] Furthermore, for the classification scenario corresponding to the number of signalized intersections between adjacent stations in a specific factor, T OD is the actual travel time between two adjacent stations on the tram line;

[0022] For the classification scenarios corresponding to the traffic flow characteristics during peak and off-peak periods in specific factors, T OD It is the actual travel time of a modern tram between the starting station and the terminal station along the entire line.

[0023] Furthermore, the analysis of the travel time reliability characteristics comprises the following steps:

[0024] Based on the planned arrival time of the tram at each station, calculate the planned travel time T between different ODs OD计 ;

[0025] Based on the planned travel time TOD计 , calculate the planned travel time t per unit mileage between different ODs 计 ,

[0026] t 计 =T OD计 / L OD

[0027] Among them, L OD is the distance between the departure site and the destination site;

[0028] Calculate the actual travel time per unit mileage of the tram t is less than the planned travel time per unit mileage t 计 The probability R (t) , whose expression is:

[0029]

[0030] Based on the probability R (t) Quantify tram travel time reliability and analyze the impact of specific factors on modern tram travel time reliability.

[0031] Furthermore, for the classification scenario corresponding to the number of signalized intersections between adjacent stations in a specific factor, T OD计 is the planned travel time between two adjacent stations on the tram line;

[0032] For the classification scenarios corresponding to the traffic flow characteristics during peak and off-peak periods in specific factors, T OD计 It is the planned travel time of a modern tram between the starting station and the terminal station along the entire line.

[0033] Furthermore, constructing the optimal distribution model of tram travel time in each classification scenario includes the following steps:

[0034] Based on the actual travel time t in different classification scenarios, frequency distribution histograms are drawn respectively, and based on the shape of the distribution histogram, a distribution type is selected to perform statistical fitting on the travel time;

[0035] The Kolmogorov-Smimov correction test and the Anderon-Darling test were used to test the goodness of fit at a significance level of α=5%, and the optimal distribution model corresponding to the actual travel time t per unit mileage of trams in each classification scenario was determined.

[0036] Furthermore, the distribution types include normal distribution, logarithmic distribution and gamma distribution.

[0037] Furthermore, the analysis of the change rules of the mean travel time and the travel time volatility includes the following steps:

[0038] Based on the optimal distribution model corresponding to the actual travel time t per unit mileage of trams in each classification scenario, that is, the sample data, the expected value and standard deviation of the actual travel time t are calculated respectively;

[0039] Based on the expected value and standard deviation, the mean and volatility of tram travel time in each classification scenario are calculated, and the changing patterns of the mean and volatility of tram travel time are analyzed.

[0040] Furthermore, based on the travel time reliability, travel time mean, travel time volatility and the characteristics of the actual tram route section, an optimized intersection signal control strategy is obtained, specifically:

[0041] Based on the existing signalized intersection control methods, i.e., no priority, relative priority, or absolute priority, the selection of intersection signal control methods is optimized and a signal control scheme generation process is proposed;

[0042] The signal control scheme generation process includes the following steps:

[0043] (1) Obtain the grade of the intersecting road;

[0044] (2) Determine whether the grade of the tram road is lower than the grade of the intersecting road. If so, select the no priority control strategy and end the entire process. Otherwise, execute step (3);

[0045] (3) Determine whether the grade of the tram road is equal to the grade of the intersecting road. If so, execute step (4); otherwise, execute step (5);

[0046] (4) Determine whether the number of intersections in the interval is greater than or equal to 2. If so, select the absolute priority control strategy and end the entire process. Otherwise, select the relative priority control strategy and end the entire process.

[0047] (5) Determine whether the number of intersections in the interval is greater than or equal to 2. If so, select the absolute priority control strategy and end the entire process. Otherwise, execute step (6);

[0048] (6) Determine whether the road grade where the tram is located is a main road. If so, execute step (7); otherwise, select a relative priority control strategy and end the entire process;

[0049] (7) Determine whether the intersecting road is a branch road. If so, select the absolute priority control strategy and end the entire process. Otherwise, determine that the intersecting road is a secondary road, select the relative priority control strategy and end the entire process.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention analyzes the influencing factors of tram travel time and constructs the optimal distribution model of tram travel time under various classification scenarios. Based on statistical and probabilistic methods, the influence of different scenarios on the reliability of tram travel time is quantitatively analyzed. Combined with the actual situation of the line, a dynamic optimization strategy for intersection signal control is proposed, which can reduce the delay and fluctuation of tram travel time, effectively improve the reliability of modern tram travel time, and is conducive to improving the punctuality rate of trams in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is an implementation flow chart of the present invention;

[0053] Figure 2 Classification of factors affecting travel time of modern trams in an embodiment of the present invention;

[0054] Figure 3 The frequency distribution histogram and fitting curve of the segment travel time in the first group of scenarios in the embodiment of the present invention;

[0055] Among them, (a) corresponds to the interval of N = 0, (b) corresponds to the interval of N = 1, (c) corresponds to the interval of N = 2, and (d) corresponds to the interval of N = 3;

[0056] Figure 4 The frequency distribution histogram and fitting curve of the full-trip travel time of the route in the second group of scenarios in the embodiment of the present invention;

[0057] Among them, (e) corresponds to the morning peak period, (f) corresponds to the evening peak period, and (g) corresponds to the non-peak period;

[0058] Figure 5 are the expected value and standard deviation of the segment travel time in the first group of scenarios in the embodiment of the present invention;

[0059] Among them, (1) corresponds to the first set of expected values, (2) corresponds to the first set of standard deviations;

[0060] Figure 6 are the expected value and standard deviation of the segment travel time in the second group of scenarios in the embodiment of the present invention;

[0061] Among them, (3) corresponds to the second set of expected values, and (4) corresponds to the second set of standard deviations;

[0062] Figure 7 A flow chart is generated for the signalized intersection control strategy optimized by the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0064] A tram intersection control strategy optimization method comprises the following steps:

[0065] S1. Analyze the factors affecting tram travel time;

[0066] S2. Based on the influencing factors, select specific factors from the influencing factors, classify the tram travel time according to different scenarios corresponding to the specific factors, and obtain multiple classification scenarios; in this embodiment, the specific factors include the number of signalized intersections between adjacent stations and traffic flow characteristics during peak and off-peak periods.

[0067] S3. Based on the tram line and operation information, the travel time of the tram in each classification scenario is counted and the reliability of the travel time is analyzed; the steps include:

[0068] S31, based on the actual time when the tram arrives at each station, calculate the actual travel time T between different tram traffic start and end points, that is, different OD OD (min);

[0069] S32, based on the actual driving time T OD , calculate the actual travel time t (min / km) per unit mileage between different ODs;

[0070] t=T OD / L OD

[0071] Among them, L OD (km) is the distance between the departure station and the destination station;

[0072] For the classification scenario corresponding to the number of signalized intersections between adjacent stations, T OD is the actual travel time between two adjacent stations on the tram line; for the classification scenarios corresponding to the traffic flow characteristics during peak and off-peak periods, T OD It is the actual travel time of a modern tram between the starting station and the terminal station along the entire line.

[0073] S33. Count the sample data of the actual travel time t per unit mileage in each classification scenario, where the sample data includes the sample quantity, sample mean, and sample standard deviation.

[0074] S34, based on the planned time for the tram to arrive at each station, calculate the planned travel time T between different ODs OD计 (min).

[0075] S35, based on the planned travel time T OD计 , calculate the planned travel time t per unit mileage between different ODs 计 (min / km),

[0076] t 计 =T OD计 / L OD

[0077] For the classification scenario corresponding to the number of signalized intersections between adjacent stations, T OD计 is the planned travel time between two adjacent stations on the tram line; for the classification scenarios corresponding to the traffic flow characteristics during peak and off-peak periods, T OD计 It is the planned travel time of a modern tram between the starting station and the terminal station along the entire line.

[0078] S36, calculating that the actual travel time per unit mileage of the tram t is less than the planned travel time per unit mileage t 计 The probability R (t) , whose expression is:

[0079]

[0080] S37, based on probability R (t) Quantify tram travel time reliability and analyze the impact of specific factors on modern tram travel time reliability.

[0081] S4. Constructing an optimal distribution model of tram travel time under each classification scenario, including the following steps:

[0082] S41, based on the actual travel time t per unit mileage in different classification scenarios, draw frequency distribution histograms respectively, and select a distribution type to perform statistical fitting on the travel time based on the shape of the distribution histogram;

[0083] S42. The Kolmogorov-Smimov correction test (KS correction test) and the Anderon-Darling test (AD test) are used to test the goodness of fit under the premise of a significance level of α=5%, and the optimal distribution model corresponding to the actual travel time t per unit mileage of trams under each classification scenario is determined.

[0084] In this embodiment, the distribution types include normal distribution, logarithmic distribution and gamma distribution.

[0085] S5. Based on the optimal distribution model, the fitting parameters of the travel time distribution under each classification scenario are calculated, and the change law of the mean travel time and the travel time volatility is analyzed, including the following steps:

[0086] S51, based on the optimal distribution model corresponding to the actual travel time t per unit mileage of the tram in each classification scenario, that is, the sample data, respectively calculating the expected value and standard deviation of the actual travel time t;

[0087] S52. Based on the expected value and standard deviation, calculate the mean and volatility of the tram travel time in each classification scenario, and analyze the changing patterns of the mean and volatility of the tram travel time.

[0088] S6. Based on the reliability of travel time, the mean of travel time, the volatility of travel time and the characteristics of the actual tram route section, the optimized intersection signal control strategy is obtained, which is as follows:

[0089] Based on the existing signalized intersection control methods, i.e., no priority, relative priority, or absolute priority, the selection of intersection signal control methods is optimized and a signal control scheme generation process is proposed;

[0090] The signal control scheme generation process is as follows: Figure 7 As shown, the following steps are included:

[0091] (1) Obtain the grade of the intersecting road;

[0092] (2) Determine whether the grade of the tram road is lower than the grade of the intersecting road. If so, select the no priority control strategy and end the entire process. Otherwise, execute step (3);

[0093] (3) Determine whether the grade of the tram road is equal to the grade of the intersecting road. If so, execute step (4); otherwise, execute step (5);

[0094] (4) Determine whether the number of intersections in the interval is greater than or equal to 2. If so, select the absolute priority control strategy and end the entire process. Otherwise, select the relative priority control strategy and end the entire process.

[0095] (5) Determine whether the number of intersections in the interval is greater than or equal to 2. If so, select the absolute priority control strategy and end the entire process. Otherwise, execute step (6);

[0096] (6) Determine whether the road grade where the tram is located is a main road. If so, execute step (7); otherwise, select a relative priority control strategy and end the entire process;

[0097] (7) Determine whether the intersecting road is a branch road. If so, select the absolute priority control strategy and end the entire process. Otherwise, determine that the intersecting road is a secondary road, select the relative priority control strategy and end the entire process.

[0098] like Figure 1As shown, as a preferred embodiment, a modern tram intersection control strategy optimization method based on travel time reliability includes the following steps:

[0099] Step 1: Analyze the factors affecting the travel time of modern trams and classify the travel time of modern trams according to different scenarios;

[0100] Step 2: Collect the route and operation information of Zhangjiang Tram Line 1, conduct descriptive statistics on its travel time in different scenarios, and analyze its travel time reliability;

[0101] Step 3: Determine the optimal distribution model of tram travel time in each scenario;

[0102] Step 4: Calculate the corresponding fitting parameters according to the travel time distribution in different scenarios, and analyze the changing patterns of the mean and volatility of travel time;

[0103] Step 5: Comprehensively analyze the impact of different factors on the three characteristics of modern tram travel time reliability, mean, and volatility, and obtain the optimized intersection signal control strategy generation process based on the influencing factors and the actual line section characteristics of Zhangjiang Tram Line 1.

[0104] In step 1, modern tram travel time is classified, such as Figure 2 As shown, the specific process is:

[0105] 1.1: The factors affecting the travel time of modern trams are divided into two categories: static factors and dynamic factors. The static factors include the right-of-way form, line laying method, number of stations, etc. The dynamic factors include the characteristics of tram drivers, tram vehicle characteristics, tram passenger flow demand, road traffic flow characteristics, intersection signal control method, etc.

[0106] 1.2: Once a modern tram is built and put into operation, the static factors will remain basically unchanged, and the dynamic factors such as the characteristics of tram drivers, tram vehicle characteristics, tram passenger flow demand, and signal control methods will also remain stable in the short term;

[0107] 1.3: This embodiment selects "the number of signalized intersections between adjacent stations" and "traffic flow characteristics during peak and off-peak periods" as specific factors, and uses the different scenarios corresponding to these two factors as the basis for the reliability analysis and data classification collection of modern tram travel time, and conducts a classified discussion on the reliability of modern tram travel time;

[0108] 1.4: The modern tram travel time is divided into two groups, with a total of 7 scenarios: the first group contains 4 scenarios: N = 0, 1, 2, 3 (N is the number of signalized intersections between adjacent stations), and the second group contains 3 scenarios: morning peak period (7:00-9:00), evening peak period (17:00-19:00), and non-peak period.

[0109] In step 2, descriptive statistics are used to calculate the travel time of Zhangjiang Tram Line 1 in different scenarios. The specific process is as follows:

[0110] 2.1: Calculate the travel time T between different ODs based on the actual time it takes for modern trams to arrive at each station OD For different traffic scenarios corresponding to the number of signalized intersections between adjacent stations, T OD is the actual travel time between two adjacent stations on a modern tram line; for different traffic scenarios corresponding to “peak and off-peak traffic flow characteristics”, T OD It is the actual travel time of a modern tram from the starting station to the terminal station along the entire line;

[0111] 2.2: After excluding traffic data with invalid or missing basic information, the actual travel time per unit mileage t (min / km) between different ODs is calculated as shown in formula (1);

[0112] t=T OD / L OD (1)

[0113] Among them, L OD (km) is the distance between the departure station and the destination station;

[0114] 2.3: Descriptive statistics of travel time data under different scenarios are shown in Table 1. The statistics include sample number, sample mean and sample standard deviation;

[0115] Table 1 Zhangjiang tram travel time statistics

[0116]

[0117]

[0118] 2.4: Analysis of Table 1 shows that when the number of signalized intersections between adjacent stations increases, or when the period changes from non-peak to peak hours, the mean and uncertainty of travel time will increase.

[0119] In step 2, the travel time reliability of Zhangjiang Tram Line 1 in different scenarios is analyzed. The specific process is as follows:

[0120] 2.5: Calculate the travel time T between different ODs based on the planned arrival time of modern trams at each station OD计 For different traffic scenarios corresponding to the number of signalized intersections between adjacent stations, T OD计 is the planned travel time between two adjacent stations on a modern tram line; for different traffic scenarios corresponding to “peak and off-peak traffic flow characteristics”, T OD计 It is the planned travel time of modern trams between the starting station and the terminal station of the entire line;

[0121] 2.6: After excluding traffic data with invalid or missing basic information, calculate the planned travel time t between different ODs 计 (min / km), as in formula (2);

[0122] t 计 =T OD计 / L OD (2)

[0123] 2.7: Quantifying travel time reliability R using probability (t) , calculate the probability that a modern tram will complete a trip within a specified time for a given OD when the road section capacity changes randomly, as shown in formula (3);

[0124]

[0125] The travel time reliability under 7 scenarios is shown in Table 2;

[0126] Table 2 Zhangjiang tram travel time reliability

[0127]

[0128]

[0129] 2.8: Analysis of Table 2 shows that the reliability of travel time is high (0.8<R≤1) in N=0,1 and non-peak hours, medium (0.5<R≤0.8) in morning and evening peak hours, and low (0≤R≤0.5) in N=2,3. Compared with the reliability of non-peak travel time, the reliability of travel time in morning and evening peak hours is reduced by 20%; compared with the case of N=1, the reliability of travel time in the case of N=2 is reduced by 74%. The results show that the reliability of travel time will be reduced in intervals with a high number of intersections (N≥2) and peak hours, and the impact is particularly obvious when N≥2, which requires special attention.

[0130] In step 3, the optimal distribution model of modern tram travel time under each scenario is determined. The specific process is as follows:

[0131] 3.1: Draw the travel time frequency distribution histograms for different scenarios, such as Figure 3 , Figure 4 shown. Figure 3 Frequency distribution histogram of segment travel time and its fitting curve when N = 0, 1, 2, 3, where (a) corresponds to the N = 0 interval, (b) corresponds to the N = 1 interval, (c) corresponds to the N = 2 interval, and (d) corresponds to the N = 3 interval;

[0132] Figure 4 The frequency distribution histogram and fitting curve of the full-trip travel time of the route during the morning and evening peak and non-peak periods; (e) corresponds to the morning peak period, (f) corresponds to the evening peak period, and (g) corresponds to the non-peak period;

[0133] 3.2: By Figure 3 , Figure 4 It can be seen that the tram travel time data under different conditions all show the characteristics of bias and long tail. When the number of signal intersections between adjacent stations increases and the flat peak period turns into a peak period, the peak point of the fitting curve moves to the right, the travel time increases overall, and the distribution becomes more dispersed, which is consistent with the analysis results in 2.4;

[0134] 3.3: According to Figure 3 , Figure 4 The distribution shape in the data set was fitted by normal distribution, lognormal distribution and gamma distribution respectively. In order to determine the goodness of fit, KS correction test and AD test were used for analysis at the significance level of α=5%, and the results are shown in Table 3;

[0135] Table 3 Results of KS correction test and AD test

[0136]

[0137]

[0138] 3.4: Analysis of Table 3 shows that, except for N=2, the other six scenarios all pass the lognormal test of KS correction and AD, and the p-value corresponding to the lognormal distribution under the two test methods is larger, indicating that the distribution model fits the travel time distribution better. The test pass rates of the two groups of normal distributions are 65% and 83.3% respectively, the test pass rates of the positive normal distribution are 87.5% and 100% respectively, and the test pass rates of the gamma distribution are 65% and 100% respectively. It can be shown that the lognormal distribution is the preferred fitting model for the travel time data distribution of Zhangjiang tram.

[0139] In step 4, the fitting parameters of travel time distribution under different scenarios are calculated and the changing rules of the mean and volatility of travel time are analyzed. The specific process is as follows:

[0140] 4.1: Based on the probability density of the lognormal distribution, calculate the expected value E(t) and standard deviation σ of the Zhangjiang tram travel time under the lognormal distribution t , as shown in formula (4), (5), and (6) respectively;

[0141]

[0142]

[0143]

[0144] Where t is the travel time of the road section, a is the standard deviation of lnt, and b is the expected value of lnt.

[0145] The calculation results are as follows Figure 5 , Figure 6 As shown; where (1) corresponds to the first set of expected values, (2) corresponds to the first set of standard deviations; (3) corresponds to the second set of expected values, and (4) corresponds to the second set of standard deviations.

[0146] 4.2: By Figure 5 , Figure 6 It can be seen that the mean travel time is reflected by the expected value E(t): when the number of signalized intersections N changes, the growth rate of the mean travel time is 17% (N changes from 0 to 1), 47% (N changes from 1 to 2), and 4% (N changes from 2 to 3). When the tram operation time changes, the growth rate of the mean travel time is 5% (off-peak time to morning peak time) and 4% (off-peak time to evening peak time); the volatility of travel time is reflected by the standard deviation σ t Reflection: When the number of signalized intersections N changes, the growth rates of travel time fluctuations are 21.1% (N changes from 0 to 1), 57.9% (N changes from 1 to 2), and 28.6% (N changes from 2 to 3). When the tram operation period changes, the growth rates of travel time fluctuations are 9% (off-peak period changes to morning peak period) and 18% (off-peak period changes to evening peak period);

[0147] 4.3: Combined with the travel time reliability analysis in 2.8, it is found that the decrease in travel time reliability is accompanied by the increase in the mean travel time and travel time fluctuation. The impact of the N value on travel time reliability is reflected in the increase in the mean travel time and travel time fluctuation, while the impact of the operating period on travel time reliability is mainly reflected in the increase in travel time fluctuation;

[0148] 4.4: It is worth noting that the number of signalized intersections that a tram passes through during its entire journey is significantly greater than 3. In theory, the travel time will be more disturbed than the travel time in the N=3 interval, that is, the standard deviation will definitely be higher than the standard deviation of the travel time in the N=3 interval. However, the fitting results show that for the N=3 interval, the travel time fitting standard deviation exceeds 0.5min / km, which is higher than the standard deviation of the tram's entire travel time during peak and non-peak hours. The main reason is that during driving, the driver will notice the deviation between the train's driving and the timetable. When the deviation is large, the driving speed will be intentionally adjusted. In addition, when arriving at the station, if you find that you are early, you can stay at the station for a while until the departure time is on time. Since the standard deviation of the entire journey time depends on the time when the train arrives at the last stop, the above factors lead to the standard deviation of the tram's travel time during the entire journey being moderated.

[0149] In step 5, the optimized intersection signal control strategy is obtained based on the travel time reliability and the characteristics of the actual operating section. The specific process is as follows:

[0150] The influence of different factors on the reliability, mean and volatility of modern tram travel time is comprehensively analyzed, and the optimized intersection signal control strategy is obtained according to the influencing factors and the actual line characteristics of Zhangjiang Tram Line 1.

[0151] 5.1: Combining the analysis results in 2.4, 2.8, 3.2, 3.4, 4.2, 4.3, and 4.4, it can be seen that the reliability of Zhangjiang tram travel time is significantly reduced in sections with a high number of signalized intersections (N≥2) and during peak hours. It is recommended to take intersection signal priority measures during peak hours and ensure the traffic efficiency of social vehicles as much as possible. Especially in the N≥2 section, it is necessary to increase the signal priority of the tram and adjust the timetable accordingly so that the tram can better meet the operation requirements of the timetable and improve the punctuality of operation;

[0152] 5.2: Considering the reliability, mean and volatility of Zhangjiang tram travel time and the road grade characteristics of the actual operation section, the selection of intersection signal control mode is optimized based on the existing signal intersection control mode, i.e. no priority, relative priority or absolute priority, and the signal control scheme generation process is proposed, such as Figure 7 As shown;

[0153] 5.3: It is recommended to implement the following measures for Zhangjiang Tram Line 1 during peak hours (7:00-9:00, 17:00-19:00): Figure 7 The signal control scheme generation process shown in the figure determines the priority form of the line passing through each signal intersection, reduces the waiting time of trams at intersections, and improves traffic efficiency. The optimized signal intersection control method is shown in Table 4.

[0154] Table 4 Optimization scheme of signal intersection control method for Zhangjiang tram line

[0155]

[0156]

[0157] Based on tram operation data and line information, this embodiment directly quantifies and analyzes the impact of different factors on three characteristics of modern tram travel time from a statistical and probabilistic perspective, and comprehensively considers the actual road conditions to optimize the signal control method of trams at signal intersections, so as to reduce delays and fluctuations in modern tram travel time and improve its punctuality.

[0158] The present invention defines the reliability of tram travel time and its analysis method, reveals the static and dynamic factors affecting the reliability of travel time, quantitatively studies the impact of the number of intersections between adjacent stations and the characteristics of traffic flow in different time periods on the reliability of tram travel time, and provides a decision-making basis for using the optimal signal control method to improve the punctuality of trams. Zhangjiang tram line facilities, 10-day tram planned operation timetable and performance data were collected. The statistical analysis method of travel time reliability was used to analyze the distribution characteristics of tram travel time under 7 typical scenarios (i.e., 4 adjacent station sections with 0, 1, 2, and 3 intersections between two adjacent stations, non-peak hours, morning peak hours, and evening peak hours). The results show that the log-normal distribution can more effectively describe the travel time distribution characteristics of trams in various scenarios; the travel time unreliability increases during the morning and evening peak hours on weekdays, and the more intersections there are between adjacent stations, the more significant the travel time unreliability increases. It is therefore suggested that appropriate intersection signal control strategies should be adopted according to the number of intersections and traffic flow characteristics. For example, when there are multiple intersections between adjacent stations during peak hours, the tram phases of these intersections can be coordinated and a green wave priority control strategy can be adopted.

[0159] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A tram intersection control strategy optimization method, characterized in that: The following steps are involved: Analyze the factors affecting tram travel time; Selecting specific factors from the influencing factors, classifying the tram travel time according to different scenarios corresponding to the specific factors, and obtaining multiple classification scenarios; Based on the tram line and operation information, the travel time of the tram in each classification scenario is counted and the reliability of the travel time is analyzed; Construct the optimal distribution model of tram travel time under each classification scenario; Based on the optimal distribution model, the fitting parameters of the travel time distribution in each classification scenario are calculated, and the change rules of the mean travel time and the travel time volatility are analyzed; Based on the travel time reliability, travel time mean, travel time volatility and characteristics of the actual tram route section, an optimized intersection signal control strategy is obtained; The specific factors include the number of signalized intersections between adjacent stations and the characteristics of traffic flow during peak and off-peak hours; The method of counting the travel time of trams in various classification scenarios based on tram lines and operation information includes the following steps: Based on the actual time when the tram arrives at each station, calculate the actual travel time T between different ODs OD ; Based on the actual driving time T OD , calculate the actual travel time t per unit mileage between different ODs; t=T OD / L OD Among them, L OD is the distance between the departure site and the destination site; Collecting sample data of actual travel time t per unit mileage in each classification scenario, wherein the sample data includes sample quantity, sample mean value and sample standard deviation; The construction of the optimal distribution model of tram travel time under each classification scenario includes the following steps: Based on the actual travel time t in different classification scenarios, frequency distribution histograms are drawn respectively, and based on the shape of the distribution histogram, a distribution type is selected to perform statistical fitting on the travel time; The Kolmogorov-Smimov correction test and the Anderon-Darling test were used to test the goodness of fit at a significance level of α = 5% to determine the optimal distribution model corresponding to the actual travel time t per unit mileage of trams in each classification scenario. Based on the travel time reliability, travel time mean, travel time volatility and the characteristics of the actual tram route section, the optimized intersection signal control strategy is obtained, which is specifically: Based on the existing signalized intersection control methods, i.e., no priority, relative priority, or absolute priority, the selection of intersection signal control methods is optimized and a signal control scheme generation process is proposed; The signal control scheme generation process includes the following steps: (1) Obtain the grade of the intersecting road; (2) Determine whether the grade of the tram road is lower than the grade of the intersecting road. If so, select the no priority control strategy and end the entire process. Otherwise, execute step (3); (3) Determine whether the grade of the tram road is equal to the grade of the intersecting road. If so, execute step (4); otherwise, execute step (5); (4) Determine whether the number of intersections in the interval is greater than or equal to 2. If so, select the absolute priority control strategy and end the entire process. Otherwise, select the relative priority control strategy and end the entire process. (5) Determine whether the number of intersections in the interval is greater than or equal to 2. If so, select the absolute priority control strategy and end the entire process. Otherwise, execute step (6); (6) Determine whether the road grade where the tram is located is a main road. If so, execute step (7); otherwise, select a relative priority control strategy and end the entire process; (7) Determine whether the intersecting road is a branch road. If so, select the absolute priority control strategy and end the entire process. Otherwise, determine that the intersecting road is a secondary road, select the relative priority control strategy and end the entire process.

2. A tram intersection control strategy optimization method according to claim 1, characterized in that: For the classification scenario corresponding to the number of signalized intersections between adjacent stations in a specific factor, T OD is the actual travel time between two adjacent stations on the tram line; For the classification scenarios corresponding to the traffic flow characteristics during peak and off-peak periods in specific factors, T OD It is the actual travel time of a modern tram between the starting station and the terminal station along the entire line.

3. A tram intersection control strategy optimization method according to claim 1, characterized in that: The analysis of the travel time reliability characteristics comprises the following steps: Based on the planned arrival time of the tram at each station, calculate the planned travel time T between different ODs OD计 ; Based on the planned travel time T OD计 , calculate the planned travel time t per unit mileage between different ODs 计 , t 计 =T OD计 / L OD Among them, L OD is the distance between the departure site and the destination site; Calculate the actual travel time per unit mileage of the tram t is less than the planned travel time per unit mileage t 计 The probability R (t) , whose expression is: Based on the probability R (t) Quantify tram travel time reliability and analyze the impact of specific factors on modern tram travel time reliability.

4. A tram intersection control strategy optimization method according to claim 3, characterized in that: For the classification scenario corresponding to the number of signalized intersections between adjacent stations in a specific factor, T OD计 is the planned travel time between two adjacent stations on the tram line; For the classification scenarios corresponding to the traffic flow characteristics during peak and off-peak periods in specific factors, T OD计 It is the planned travel time of a modern tram between the starting station and the terminal station along the entire line.

5. A tram intersection control strategy optimization method according to claim 1, characterized in that: The distribution types include normal distribution, logarithmic distribution and gamma distribution.

6. A tram intersection control strategy optimization method according to claim 1, characterized in that: The analysis of the change rules of the mean travel time and the travel time volatility comprises the following steps: Based on the optimal distribution model corresponding to the actual travel time t per unit mileage of trams in each classification scenario, that is, the sample data, the expected value and standard deviation of the actual travel time t are calculated respectively; Based on the expected value and standard deviation, the mean and volatility of tram travel time in each classification scenario are calculated, and the changing patterns of the mean and volatility of tram travel time are analyzed.

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