A method for estimating road capacity and bottleneck status considering the influence of longitudinal slope

Abstract

This invention discloses a method for estimating road capacity and bottleneck status considering the influence of longitudinal slope. The steps include: 1. Collecting longitudinal slope data of the target road; 2. Identifying longitudinal slope regions using wavelet transform; 3. Collecting traffic flow information under free-flow conditions of the target road; 4. Identifying macroscopic traffic flow velocity regions using wavelet transform; 5. Constructing a capacity model considering the influence of longitudinal slope; 6. Predicting traffic bottlenecks of the target road based on the current state and inflow traffic volume. This invention achieves accurate matching of road capacity under different road longitudinal slopes and free-flow velocities. By using initial state and real-time inflow statistics, it enables the prediction of bottleneck road segment status, providing important data for traffic management.

Description

Technical Field

[0001] This invention relates to a method for estimating road capacity and bottleneck status considering the influence of longitudinal slope, belonging to the field of intelligent transportation technology. Background Technology

[0002] In road design and traffic planning, longitudinal slope is a crucial factor that significantly impacts traffic flow and capacity. Traffic bottlenecks on sloping road sections are often caused by a combination of factors, including speed limits, speed differences between vehicles, and variations in free-flow velocity. Therefore, studying traffic bottlenecks on sloping road sections and exploring ways to alleviate this congestion is an important aspect of improving road traffic efficiency.

[0003] Traditional capacity models based on longitudinal slope often neglect changes in free-flow velocity under road conditions, only considering the impact of longitudinal slope on vehicle speed. This leads to insufficient accuracy in capacity calculations and difficulty in accurately capturing traffic bottlenecks. In fact, on sloping road sections, the impact of the slope on vehicle free-flow velocity causes changes, thus affecting the overall traffic flow. Therefore, to more accurately assess the capacity of sloping road sections and predict traffic bottlenecks, it is necessary to consider the impact of longitudinal slope on free-flow velocity and establish a more comprehensive and accurate capacity model. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention proposes a method for estimating road capacity and bottleneck status that considers the influence of longitudinal slope. This method aims to comprehensively consider the impact of longitudinal slope on free-flow velocity, integrate longitudinal slope factors into capacity calculations, better reflect traffic flow characteristics in actual road environments, and effectively guide traffic planning and management, optimize road design, thereby alleviating traffic congestion, improving road efficiency, and enhancing the overall operational level of the transportation system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The method for estimating road capacity and bottleneck status considering the influence of longitudinal slope in this invention is characterized by the following steps:

[0007] Step 1: Collect the position data of the experimental vehicle on the target road at time t {s} t ,α t}; where s t Let α represent the arc length of the experimental vehicle at time t. t This represents the longitudinal slope of the experimental vehicle at time t;

[0008] Step 2: Apply wavelet transform to {s} t ,α tBoundary detection is performed, and all detected boundary points are added to the slope change point set β, which is used to identify the longitudinal slope region dataset Ω1.

[0009] Step 3: Collect free-flow trajectory videos of the target road affected by the longitudinal slope, and obtain the vehicle trajectory dataset Φ from them;

[0010] Step 4: Use wavelet transform to detect the points where the vehicle's trajectory velocity changes in Φ, determine the location of the free-flow velocity change, and thus construct the velocity region dataset Ω;

[0011] Step 5: Establish a road capacity model that considers longitudinal slope and free flow velocity, and calculate the capacity U of each segment of the target road based on Ω. Ψ ;

[0012] Step 6: Identify and predict road bottleneck status, and obtain all bottleneck statuses and updated traffic flows for each segment of the target road during the time period [0, T1].

[0013] The method for estimating road capacity and bottleneck status considering the influence of longitudinal slope as described in this invention is characterized in that step 1 includes:

[0014] Step 1.1: Use the inertial navigation system on the experimental vehicle to acquire the driving data of the experimental vehicle as it travels along the target road [λ]. t ,φ t ,φ t ], where λ t Let φ be the longitude of the experimental vehicle at time t. t Let h be the latitude of the experimental vehicle at time t. t Let t be the elevation of the experimental vehicle at time t, where t is the current time being recorded, t∈[0,T]; and T is the total number of times.

[0015] Step 1.2: Obtain the geographical coordinates of the experimental vehicle at time t using equation (1): Linet = [x t ,y t ,z t ], where x t Let y be the east-west position of the experimental vehicle in the geocentric coordinate system at time t. t Let z be the north-south position of the experimental vehicle in the geocentric coordinate system at time t. t Let t be the altitude position of the experimental vehicle in the geocentric coordinate system at time t;

[0016]

[0017] In equation (1), It is the radius of the Earth's geoid curvature. is the first eccentricity of the Earth's ellipsoid, a is the length of the major axis of the Earth's ellipsoid, and b is the length of the minor axis of the Earth's ellipsoid.

[0018] Step 1.3: Calculate the position data of the experimental vehicle at time t using equation (2) {s}. t ,α t}, t∈[0,T-1];

[0019]

[0020] In equation (2), s t This represents the position of the experimental vehicle from its initial position [x0, y0] to its position [x] at time t. t ,y t The arc length of ] is initialized to s0 = 0, α t The position of the experimental vehicle at time t is represented by [x]. t ,y t The position of the experimental vehicle at time t+1 [x] t+1 ,y t+1 The longitudinal slope of ].

[0021] Step 2 is performed as follows:

[0022] Step 2.1: Calculate {s} using equation (3). t ,α t The continuous wavelet transform coefficients T(δ,ε) of};

[0023]

[0024] In equation (3), δ is the scaling factor, ε is the translation factor, and ε∈[0, s T-1 ];s T-1 This represents the position of the experimental vehicle from its initial position [x0, y0] to its position [x] at time T-1. T-1 ,y T-1 The arc length of ];

[0025] Step 2.2: Calculate the wavelet energy distribution E of the longitudinal slope curve using equation (4). ε ;

[0026]

[0027] In equation (4), δ min It is the minimum value of the scaling factor, δ max It is the maximum value of the scaling factor;

[0028] Step 2.3, let When the slope change points β={ε1,ε2,…,ε are obtained, the set of change points is obtained. i ,…,ε r}, where ε i This indicates the location of the i-th slope change point, and r is the number of slope change points;

[0029] Step 2.4: Connect adjacent slope change points in β with straight lines to form the longitudinal slope line L. α ={l1,l2,…,l i ,…,l r-1}, where l i Represents the i-th longitudinal slope region [ε] i ,ε i+1 The longitudinal slope line of the i-th longitudinal slope region [ε]; i ,ε i+1 The slope of [ ] is denoted as A. i ;ε i+1 This indicates the position of the (i+1)th slope change point;

[0030] Step 2.5, when s is satisfied t ∈[ε i ,ε i+1 When ], it indicates s t In the i-th longitudinal slope region [ε i ,ε i+1 Within ], the i-th velocity region [ε i ,ε i+1 The slope A i Assigned to α t Thus, the updated location data {s} is obtained. t ,α t}; thus, the updated location dataset Ω1={s t ,α t |t∈[0,T-1]}.

[0031] Step 3 is performed as follows:

[0032] Step 3.1: Map the set of slope change points β to the target road, thus transforming it into the latitude and longitude β of the slope change points. loc =[λ loc ,φ loc ], λ loc For the longitude set of the slope change points, φ loc The set of latitudes of the slope change points;

[0033] Step 3.2: Based on the latitude and longitude β of the slope change point. loc The spacing and coverage range of the high-altitude cameras are determined to establish the set of acquisition points β' of the high-altitude camera equipment, ensuring that the viewing angle of the high-altitude camera equipment is centered on the slope change point and covers the target road.

[0034] Step 3.3: Position the high-altitude camera equipment at the collection point set β' and capture video of the free-flow trajectory of the target road upstream and downstream of β';

[0035] Step 3.4: Use the vehicle foreground algorithm to separate vehicles from roads in the upstream and downstream free-flow trajectory videos, thereby obtaining the vehicle trajectory dataset Φ.

[0036] Step 4 is performed as follows:

[0037] Step 4.1: Separate the vehicle trajectory set TJ = {tj} from the vehicle trajectory dataset Φ. n |n∈[0,N]}, where tj n Here is the trajectory data for the nth vehicle, where N is the number of trajectories;

[0038] Step 4.2, [x] t ,y t ,z t Set as the normal to the Frenet coordinate system;

[0039] Step 4.3: Convert TJ into trajectory position data in the Frenet coordinate system. F ={tj n,F |n∈[0,N]}, where tj n,F Let tj be the position data of the trajectory of the nth vehicle in the Frenet coordinate system. n,F =[s n,F ,ρ n,F ], where s n,F Given the dataset of arc lengths of the trajectory of the nth vehicle, ρ n,F This is the offset dataset of the trajectory of the nth vehicle;

[0040] Step 4.4, Calculate TJ F The normal velocity is used to construct the position and velocity dataset TJV in the Frenet coordinate system. F ={s n,F ,v n,F}, where v n,F This is a dataset of normal velocities for the trajectory of the nth vehicle.

[0041] Step 4.5: Calculate tj using equations (3) and (4). n,F Wavelet transform energy E n,ω Thus, the average transformation energy E can be obtained using equation (5). ω ;

[0042]

[0043] In equation (5), ω is the translation factor;

[0044] Step 4.6, let When the macroscopic velocity variation point of the free flow is obtained, γ = {ω1, ω2, ..., ω...} j ,…,ωq}, where ω j The arc length coordinate of the j-th velocity fluctuation point is represented by q, where q is the number of velocity fluctuation points in the free flow.

[0045] Step 4.7, Define [ω] j ,ω j+1 ] represents the j-th velocity region; [ω] j ,ω j+1 The average velocity of all trajectories within the range is taken as the velocity region [ω]. j ,ω j+1 The free-flow velocity V j ;

[0046] Step 4.8, when s is satisfied t ∈[ω j ,ω j+1 When ], it indicates s t In the j-th velocity region [ω j ,ω j+1 Within ], and the j-th velocity region [ω j ,ω j+1 The free-flow velocity V j Assigned to s t free flow velocity v t Thus, the free-flow velocity dataset Ω2={s of the experimental road section was obtained. t ,v t |t∈[0,T-1]};

[0047] Step 4.9: Merge Ω1 and Ω2 into a dataset Ω={s}, which represents the longitudinal slope and free flow velocity affected by the road. t ,α t ,v t |t∈[0,T-1]};

[0048] Step 4.10, for Ω={s t ,α t , v Repartitioning t|t∈[0,T-1]}:

[0049] When α t =α t+1 And v t =v t+1 When, it means s t and s t+1 On road sections with the same longitudinal slope and free-flow velocity, and s t and s t+1 The road segments are merged to obtain the merged road segment dataset Ψ={Ψ1,Ψ2,…,Ψ m …,Ψ M}, M is the total number of road segments after the merger; Ψm Let Ψ represent the m-th merged road segment, and let Ψ be the length of the merged segment. m =[S m ,S m+1 ,v m ,α m ], S m Let S be the starting arc length coordinate of the m-th merged road segment. m+1 Let v be the coordinate of the endpoint arc length of the m-th merged road segment. m Let α be the free-flow velocity of the m-th merged road segment. m Let be the longitudinal slope of the m-th merged road segment.

[0050] Step 5 is performed as follows:

[0051] The m-th merged road segment Ψ is calculated using equation (6). m U-passage capacity m Thus, the traffic capacity U of each section of the target road. Ψ ={U1,U2,…,U m …,U M};

[0052] U m =v m *k m (6)

[0053] In equation (6), k m For Ψ m Traffic density in free-flow state, obtained from equation (7);

[0054]

[0055] In equation (7), HW m For Ψ m The optimal average headway is obtained from equation (8);

[0056]

[0057] In equation (8), T rec Where MSD is the driver's reaction time, and MSD is the minimum safe following distance between vehicles; m For Ψ m The vehicle braking deceleration affected by the longitudinal slope is calculated using equation (9);

[0058] a m =a0+g*sin[acrtan(α) m (9)

[0059] In equation (9), a0 is the vehicle braking deceleration when the longitudinal slope is 0; g represents the acceleration due to gravity.

[0060] Step 6 is performed as follows:

[0061] Step 6.1: Collect the traffic flow c of each segment of the target road at the current time 'time'. Ψ ={c1,c2,…,c m …,c M}; where c m Represents the m-th merged road segment Ψ m Traffic;

[0062] Define the bottleneck state B = {b1, b2, ..., b} of each segment in the target road. m …,b M}, where b m Represents the m-th merged road segment Ψ k The bottleneck state; initialize b m =0;

[0063] Step 6.2: Set time = 0, and set the maximum update duration to T1;

[0064] Step 6.3: Use roadside equipment to obtain the inflow flow c0 of the merged road segment dataset Ψ within the time period [time, time+Δt], where Δt is the interval for flow statistics;

[0065] Step 6.4, Determine c Ψ Ψ If the condition is met, it means there is no traffic bottleneck on the target road, and proceed to step 6.5; otherwise, it means there is a traffic bottleneck on the target road, and proceed to step 6.6.

[0066] Step 6.5: Obtain the m-th merged road segment Ψ according to equation (10). m The update flow c' at time time+Δt m Thus, the updated flow c' of the target road at time time+Δt is obtained. Ψ ={c'1,c'2,…,c' m …,c' M Let b m After the result equals 0, proceed to step 6.11;

[0067] c′ m =c m *Δt-c m-1 *Δt m∈[1,M] (10)

[0068] Step 6.6: Initialize variable k = M;

[0069] Step 6.7: Determine the k-th merged road segment Ψ k ​Traffic c k k Does this condition hold true? If it does, it indicates that the k-th merged road segment Ψ... k No traffic bottleneck occurred, and the k-th merged road segment Ψ was updated. k bottleneck state b k =0, proceed to step 6.8; otherwise, it means Ψ k A traffic bottleneck occurred, and b was updated. k =1, proceed to step 6.10; where, U k Represents the k-th merged road segment Ψ k Traffic capacity;

[0070] Step 6.8: Determine the (k-1)th merged road segment Ψ k-1 Traffic c k-1 k-1 Does this hold true? If true, it represents the (k-1)th merged road segment Ψ. k-1 No traffic bottleneck occurred, and the kth merged road segment Ψ was obtained using equation (11). k The update flow c' at time time+Δt k Proceed to step 6.10; otherwise, it indicates the (k-1)th merged road segment Ψ k-1 A traffic bottleneck occurs, and the updated flow rate c' is obtained using equation (12). k Proceed to step 6.10, where U k-1 Represents the (k-1)th merged road segment Ψ k-1 Traffic capacity;

[0071] c′ k =(c k -c k-1 )*Δt (11)

[0072] c′ k =(c k -U k-1 )*Δt (12)

[0073] Step 6.9, Determine c k-1 k-1 Is it valid? If so, road segment Ψ k-1 No traffic bottleneck occurred, and the updated flow rate c' was obtained using equation (13). k Proceed to step 6.10; otherwise, indicate road segment Ψ. k and road section Ψ k-1 Traffic bottlenecks occurred in both cases, and the updated flow rate c' was obtained using equation (14). k Proceed to step 6.10;

[0074] c′ k =(U​​​k -c k-1 )*Δt (13)

[0075] c′ k =(U k -min(U k-1 ,U k ))*Δt (14)

[0076] Step 6.10. Determine whether k = 1 holds. If it holds, it means that the updated bottleneck state B at time and the updated flow rate c' of the merged road segment dataset Ψ in the period [time, time+Δt] are obtained Ψ ={c'1,c'2,…,c' M} and output, then transfer to Step 6.11; otherwise, assign k-1 to k, transfer to Step 6.7 and execute sequentially;

[0077] Step 6.11. Determine whether time+Δt < T1 holds. If it holds, assign time+Δt to time and c' Ψ assign to c Ψ , then transfer to Step 6.3 and execute sequentially; otherwise, it means that all bottleneck states and updated flow rates in the period [0, T1] are obtained.

[0078] An electronic device according to the present invention includes a memory and a processor, characterized in that the memory is used to store a program for supporting the processor to execute the method for estimating road traffic capacity and bottleneck state considering longitudinal slope influence, and the processor is configured to execute the program stored in the memory. [[ID=​​​​​​​​​​2. This invention comprehensively considers the influence of longitudinal slope and free-flow velocity within a road zone on braking deceleration and headway, constructing a corresponding zone capacity model. This model allows for a more comprehensive assessment of the capacity of each road zone and enables the identification and prediction of traffic bottlenecks based on inflow conditions and current status. Simultaneously, this invention requires relatively little data, improving the convenience and efficiency of practical applications and providing traffic management departments with a more practical and operable tool. Attached Figure Description

[0083] Figure 1 This is a flowchart illustrating the road zoning process based on longitudinal slope and free-flow velocity according to the present invention.

[0084] Figure 2 This is a flowchart of the bottleneck section identification and prediction method of the present invention. Detailed Implementation

[0085] In this embodiment, the following is used: Figure 1 The overall flowchart shown and Figure 2 The bottleneck section identification and prediction process is proposed, and a method for estimating road capacity and bottleneck status considering the influence of longitudinal slope is carried out in the following steps:

[0086] Step 1: Collect longitudinal slope data of the target road;

[0087] Step 1.1: Use the inertial navigation system on the experimental vehicle to acquire the driving data of the experimental vehicle as it travels along the target road [λ]. t ,φ t ,h t ], where λ t Let φ be the longitude of the experimental vehicle at time t. t Let h be the latitude of the experimental vehicle at time t. t Let t be the elevation of the experimental vehicle at time t, where t is the current time being recorded, t∈[0,T]; and T is the total number of times.

[0088] Step 1.2: Obtain the geographical coordinates of the experimental vehicle at time t using equation (1). t =[x t ,y t ,z t ], where x t Let y be the east-west position of the experimental vehicle in the geocentric coordinate system at time t. t Let z be the north-south position of the experimental vehicle in the geocentric coordinate system at time t. t Let t be the altitude position of the experimental vehicle in the geocentric coordinate system at time t;

[0089]

[0090] in, It is the radius of curvature of the zonal circle. is the first eccentricity of the ellipsoid, a is the length of the major axis of the ellipsoid, taken as 6378.137km, and b is the length of the minor axis of the ellipsoid, taken as 6356.7523km.

[0091] Step 1.3: Calculate the position data of the experimental vehicle at time t using equation (2) {s}. t ,α t}, t∈[0,T-1];

[0092]

[0093] In equation (2), s t This represents the position of the experimental vehicle from its initial position [x0, y0] to its position [x] at time t. t ,y t The arc length of ], s0=0, α t The position of the experimental vehicle at time t is represented by [x]. t ,y t The position of the experimental vehicle at time t+1 [x] t+1 ,y t+1 The longitudinal slope of ].

[0094] Step 2: Apply wavelet transform to {s} t ,α t Boundary detection is performed, and boundary points are defined as slope change points to identify longitudinal slope regions in the dataset;

[0095] Step 2.1: Calculate {s} using equation (3). t ,α t The continuous wavelet transform coefficients T(δ,ε) of};

[0096]

[0097] In equation (3), δ is the scaling factor, ε is the translation factor, and ε∈[0, s T-1 ];s T-1 This represents the position of the experimental vehicle from its initial position [x0, y0] to its position [x] at time T-1. T-1 ,y T-1 The arc length of ].

[0098] Step 2.2: Calculate the wavelet energy distribution E of the longitudinal slope curve using equation (4). ε ;

[0099]

[0100] Where, δ min It is the minimum value of the scaling factor, which is 6, δ max This is the maximum value of the scaling factor, which is 48.

[0101] Step 2.3, let When the slope change points β={ε1,ε2,…,ε are obtained, the set of change points is obtained. i ,…,ε r}, where ε i This indicates the location of the i-th slope change point, and r is the number of slope change points;

[0102] Step 2.4: Connect adjacent slope change points in β with straight lines to form the longitudinal slope line L. α ={l1,l2,…,l i ,…,l r-1}, where l i Represents the i-th longitudinal slope region [ε] i ,ε i+1 The longitudinal slope line of the i-th longitudinal slope region [ε]; i ,ε i+1 The slope of [ ] is denoted as A. i ;ε i+1 This indicates the position of the (i+1)th slope change point;

[0103] Step 2.5, when s is satisfied t ∈[ε i ,ε i+1 When ], it indicates s t In the i-th longitudinal slope region [ε i ,ε i+1 Within ], the i-th velocity region [ε i ,ε i+1 The slope A i Assigned to α t Thus, the updated location data {s} is obtained. t ,α t}; thus, the updated location dataset Ω1={s t ,α t |t∈[0,T-1]}.

[0104] Step 3: Collect free-flow trajectory data of the target road affected by longitudinal slope;

[0105] Step 3.1: Map the set of slope change points β to the target road, thus transforming it into the latitude and longitude β of the slope change points. loc =[λ loc ,φ loc ], λ loc For the longitude set of the slope change points, φ loc The set of latitudes of the slope change points;

[0106] Step 3.2: Based on the latitude and longitude β of the slope change point. locThe spacing and coverage range of the high-altitude cameras are determined to establish the set of acquisition points β' of the high-altitude camera equipment, ensuring that the viewing angle of the high-altitude camera equipment is centered on the slope change point and covers the target road.

[0107] Step 3.3: Position the high-altitude camera equipment at the collection point set β' and capture free-flow video of the target road upstream and downstream of β';

[0108] Step 3.4: Use the vehicle foreground algorithm to separate vehicles from roads in the upstream and downstream free-flow videos, thereby obtaining the vehicle trajectory dataset Φ.

[0109] Step 4: Use wavelet transform to detect the points where the vehicle's trajectory velocity changes, determine the location of the free-flow velocity change, and thus construct a velocity region dataset;

[0110] Step 4.1: Separate the vehicle trajectory set TJ = {tj} from the vehicle trajectory dataset Φ. n |n∈[0,N]}, where tj n Here is the trajectory data for the nth vehicle, where N is the number of trajectories;

[0111] Step 4.2, [x] t ,y t ,z t Set as the normal to the Frenet coordinate system;

[0112] Step 4.3: Convert TJ into trajectory position data in the Frenet coordinate system. F ={tj n,F |n∈[0,N]{,where,tj n,F Let tj be the position data of the trajectory of the nth vehicle in the Frenet coordinate system. n,F =[s n,F ,ρ n,F ], where s n,F Given the dataset of arc lengths of the trajectory of the nth vehicle, ρ n,F This is the offset dataset of the trajectory of the nth vehicle;

[0113] Step 4.4, Calculate TJ F The normal velocity is used to construct the position and velocity dataset TJV in the Frenet coordinate system. F ={s n,F ,v n,F}, where v n,F This is a dataset of normal velocities for the trajectory of the nth vehicle.

[0114] Step 4.5: Calculate tj using equations (3) and (4). n,F Wavelet transform energy E n,ωThus, the average transformation energy E can be obtained using equation (5). ω ;

[0115]

[0116] In equation (5), ω is the translation factor.

[0117] Step 4.6, let When the macroscopic velocity variation point of the free flow is obtained, γ = {ω1, ω2, ..., ω...} j ,…,ω q}, where ω j The arc length coordinate of the j-th velocity fluctuation point is represented by q, where q is the number of velocity fluctuation points in the free flow.

[0118] Step 4.7, Define [ω] j ,ω j+1 ] represents the j-th velocity region; [ω] j ,ω j+1 The average velocity of all trajectories within the range is taken as the velocity region [ω]. j ,ω j+1 The free-flow velocity V j ;

[0119] Step 4.8, when s is satisfied t ∈[ω j ,ω j+1 When ], it indicates s t In the j-th velocity region [ω j ,ω j+1 Within ], and the j-th velocity region [ω j ,ω j+1 The free-flow velocity V j Assigned to v t , where v t For s t The free-flow velocity is obtained to acquire the free-flow velocity dataset Ω2={s} of the experimental road section. t ,v t |t∈[0,T-1]};

[0120] Step 4.9: Merge Ω1 and Ω2 into a dataset Ω={s}, which represents the longitudinal slope and free flow velocity affected by the road. t ,α t ,v t |t∈[0,T-1]};

[0121] Step 4.10, for Ω={s t ,α t ,v t Repartitioning of |t∈[0,T-1]}:

[0122] When αt =α t+1 And v t =v t+1 When, it means s t and s t+1 On road sections with the same longitudinal slope and free-flow velocity, and s t and s t+1 The road segments are merged to obtain the merged road segment dataset Ψ={Ψ1,Ψ2,…,Ψ m …,Ψ M}, M is the total number of road segments after the merger; Ψ m Let Ψ represent the m-th merged road segment, and let Ψ be the length of the merged segment. m =[S m ,S m+1 ,v m ,α m ], S m Let S be the starting arc length coordinate of the m-th merged road segment. m+1 Let v be the coordinate of the endpoint arc length of the m-th merged road segment. m Let α be the free-flow velocity of the m-th merged road segment. m Let be the longitudinal slope of the m-th merged road segment.

[0123] Step 5: Establish a road capacity model that considers longitudinal slope and free flow velocity, and calculate the capacity of each segment of the target road;

[0124] The m-th merged road segment Ψ is calculated using equation (6). m U-passage capacity m Thus, the traffic capacity U of each section of the target road. Ψ ={U1,U2,…,U m …,U M};

[0125] U m =v m *k m (6)

[0126] In equation (6), k m For Ψ m Traffic density in free-flow state, obtained from equation (7);

[0127]

[0128] In equation (7), HW m For Ψ m The optimal average headway is obtained from equation (8);

[0129]

[0130] In equation (8), T rec Where MSD is the driver's reaction time, and MSD is the minimum safe following distance between vehicles; m For Ψ m The vehicle braking deceleration affected by the longitudinal slope is calculated using equation (9);

[0131] a m =a0+g*sin[acrtan(α) m (9)

[0132] In equation (9), a0 is the vehicle braking deceleration when the longitudinal slope is 0; g represents the acceleration due to gravity.

[0133] Step 6: Identify and predict road bottleneck conditions;

[0134] Step 6.1: Collect the traffic flow c of each segment of the target road at the current time 'time'. Ψ ={c1,c2,…,c m …,c M}; where c m Represents the m-th merged road segment Ψ m Traffic;

[0135] Define the bottleneck state B = {b1, b2, ..., b} of each segment in the target road. m …,b M}, where b m Represents the m-th merged road segment Ψ k The bottleneck state; initialize b m =0;

[0136] Step 6.2: Set time = 0, and set the maximum update duration to T1;

[0137] Step 6.3: Use roadside equipment to obtain the inflow flow c0 of the merged road segment dataset ψ within the time period [time, time+Δt], where Δt is the interval for flow statistics.

[0138] Step 6.4, Determine c Ψ Ψ If the condition is met, it means there is no traffic bottleneck on the target road, and proceed to step 6.5; otherwise, it means there is a traffic bottleneck on the target road, and proceed to step 6.6.

[0139] Step 6.5: Obtain the m-th merged road segment Ψ according to equation (10). m The update flow c' at time time+Δt m Thus, the updated flow c' of the target road at time time+Δt is obtained. Ψ ={c'1,c'2,…,c' m ​…,c' M Let b m After the result equals 0, proceed to step 6.11;

[0140] c′ m =c m *Δt-c m-1 *Δt m∈[1,M] (10)

[0141] Step 6.6: Initialize variable k = M;

[0142] Step 6.7: Determine the k-th merged road segment Ψ k Traffic c k k Does this condition hold true? If it does, it indicates that the k-th merged road segment Ψ... k No traffic bottleneck occurred, and the k-th merged road segment Ψ was updated. k bottleneck state b k =0, proceed to step 6.8; otherwise, it means Ψ k A traffic bottleneck occurred, and b was updated. k =1, proceed to step 6.10; where, U k Represents the k-th merged road segment Ψ k Traffic capacity.

[0143] Step 6.8: Determine the (k-1)th merged road segment Ψ k-1 Traffic c k-1 k-1 Does this hold true? If true, it represents the (k-1)th merged road segment Ψ. k-1 No traffic bottleneck occurred, and the kth merged road segment Ψ was obtained using equation (11). k The update flow c' at time time+Δt k Proceed to step 6.10; otherwise, it indicates the (k-1)th merged road segment Ψ k-1 A traffic bottleneck occurs, and the updated flow rate v' is obtained using equation (12). k Proceed to step 6.10, where U k-1 Represents the (k-1)th merged road segment Ψ k-1 Traffic capacity;

[0144] c′ k =(c k -c k-1 )*Δt (11)

[0145] c′ k =(c k -U k-1 )*Δt (12)

[0146] Step 6.9, Determine c​​k-1 <U k-1 Whether it holds. If it holds, there is no traffic bottleneck in section k - 1, and the updated flow c' is obtained using Equation (13). k , and proceed to Step 6.10; otherwise, traffic bottlenecks occur in both section k and section k - 1, and the updated flow c' is obtained using Equation (14). k , and proceed to Step 6.10.

[0147] c′ k =(U k - c k-1 ) * Δt (13)

[0148] c′ k =(U k - min(U k-1 , U k )) * Δt (14)

[0149] Step 6.10: Determine whether k = 1 holds. If it holds, it means the updated bottleneck state B at time and the updated flow c' of the merged section dataset Ψ in the period [time, time + Δt] are obtained Ψ ={c'1, c'2, …, c' M} and output, then proceed to Step 6.11; otherwise, assign k - 1 to k, and proceed to Step 6.7 and execute sequentially;

[0150] Step 6.11: Determine whether time + Δt < T1 holds. If it holds, assign time + Δt to time, and assign c' Ψ to c Ψ , and proceed to Step 6.3 and execute sequentially; otherwise, it means all bottleneck states and updated flows in the period [0, T1] are obtained.

[0151] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the dynamic calculation method, and the processor is configured to execute the program stored in the memory.

[0152] In this embodiment, a computer - readable storage medium stores a computer program, and when the computer program is run by a processor, it executes the steps of the dynamic calculation method.

Claims

1. A method for estimating road capacity and bottleneck status considering the influence of longitudinal slope, characterized in that, The procedure is as follows: Step 1, data collection Location data of the test vehicle on the target road at any time ;in, express The arc length of the test vehicle at all times, express The longitudinal slope of the test vehicle at all times; Step 1.1: Use the inertial navigation system on the experimental vehicle to acquire driving data of the experimental vehicle as it travels along the target road. ,in, for The longitude of the test vehicle at all times for The latitude of the test vehicle at all times. for The elevation of the test vehicle at all times To record the current moment, ; Total number of moments; Step 1.2, obtain from equation (1) Geographic coordinates of the time-testing vehicle ,in, for The east-west position of the experimental vehicle in the geocentric coordinate system at any given time. for The north-south position of the experimental vehicle in the geocentric coordinate system. for The altitude position of the experimental vehicle in the geocentric coordinate system at any given time; (1) In equation (1), It is the radius of the Earth's geoid curvature. It is the first eccentricity of the Earth's ellipsoid. It is the length of the major axis of the Earth's ellipsoid. It is the length of the minor axis of the Earth's ellipsoid; Step 1.3: Calculate using equation (2) Location data of the test vehicle at all times , ; (2) In equation (2), Indicates the initial position of the experimental vehicle. ]to The location of the test vehicle at all times [ The arc length of ] is initialized. , express The location of the test vehicle at all times [ ]to The location of the test vehicle at all times [ The longitudinal slope of the slope; Step 2: Perform wavelet transform on... Boundary detection is performed, and all detected boundary points are added to the set of slope change points. Data set used to identify longitudinal slope areas ; Step 2.1, calculate using equation (3) Continuous wavelet transform coefficients ; (3) In equation (3), It is a scale factor. It is the translation factor. ; Indicates the initial position of the experimental vehicle. ]to The location of the test vehicle at all times [ The arc length of ]; Step 2.2: Calculate the wavelet energy distribution of the longitudinal slope curve using equation (4). ; (4) In equation (4), It is the minimum value of the scale factor. It is the maximum value of the scaling factor; Step 2.3, let When the slope change point set is obtained, the set of slope change points can be obtained. ,in, Indicates the first Location of slope change point It is the number of slope change points; Step 2.4, Connect adjacent slope change points with straight lines to form a longitudinal slope line. ,in, Indicates the first Each longitudinal slope area The longitudinal slope line; the first Each longitudinal slope area The slope is denoted as ; Indicates the first +1 slope change point location; Step 2.5, when the condition is met When, it means In the Each longitudinal slope area Inside, the first Speed ​​Zone slope Assign to This allows us to obtain updated location data. ; and thus obtain the updated location dataset ; Step 3: Collect free-flow trajectory videos of the target road affected by the longitudinal slope, and obtain the vehicle trajectory dataset from them. ; Step 4: Use wavelet transform for detection By identifying the points where the vehicle's trajectory velocity changes, the locations of free-flow velocity changes can be determined, thereby constructing a velocity region dataset. ; Step 5: Establish a road capacity model that considers longitudinal slope and free-flow velocity, and based on... Calculate the traffic capacity of each segment of the target road. ; Step 6: Identify and predict the road bottleneck status, and obtain the bottleneck status of each segment of the target road. All bottleneck statuses and update traffic during the time period.

2. The method for estimating road capacity and bottleneck status considering the influence of longitudinal slope according to claim 1, characterized in that, Step 3 is performed as follows: Step 3.1: Set up the slope change points Mapped to the target road, thus transforming into the latitude and longitude of the slope change point. , The set of longitudes of the slope change points. The set of latitudes of the slope change points; Step 3.2: Based on the latitude and longitude of the slope change point Based on the spacing and coverage area of ​​the high-altitude cameras, determine the set of acquisition points for the high-altitude camera equipment. This ensures that the aerial camera's field of view is centered on the slope change point and covers the target road; Step 3.3: Position the high-altitude camera equipment at the collection point location. And photograph the target road in Videos of free-flow trajectories upstream and downstream; Step 3.4: Use the vehicle foreground algorithm to separate vehicles from roads in the upstream and downstream free-flow trajectory videos, thereby obtaining the vehicle trajectory dataset. .

3. The method for estimating road capacity and bottleneck status considering the influence of longitudinal slope according to claim 2, characterized in that, Step 4 is performed as follows: Step 4.1: From the vehicle trajectory dataset Vehicle trajectory set separated from ,in, For the first Vehicle trajectory data, It is the number of trajectories; Step 4.2, Set as the normal to the Frenet coordinate system; Step 4.3, Convert to trajectory position data in Frenet coordinate system ,in, For the first The vehicle's trajectory and position data in the Frenet coordinate system, and ,in, For the first Arc length dataset of vehicle trajectories For the first Offset dataset of vehicle trajectories; Step 4.4, Calculation The normal velocity is used to construct a position and velocity dataset in the Frenet coordinate system. ,in, For the first Dataset of normal velocities of vehicle trajectories; Step 4.5: Calculate using equations (3) and (4). wavelet transform energy Thus, the average transformation energy can be obtained using equation (5). ; (5) In equation (5), It is another translation factor; Step 4.6, let When the point of change of macroscopic velocity of the free flow is obtained, ,in, Indicates the first The arc length coordinates of each velocity fluctuation point It represents the number of velocity fluctuation points in the free flow; Step 4.7, Definition For the first A speed range; will The average velocity of all trajectories within the range is used as the first... Speed ​​Zone free flow velocity ; Step 4.8, when the conditions are met When, it means In the Speed ​​Zone Inside, and will the first Speed ​​Zone free flow velocity Assign to free flow velocity This allows us to obtain a dataset of free-flow velocities for the experimental road section. ; Step 4.9, Merge and Dataset of longitudinal slope and free flow velocity affected by road ; Step 4.10, for Re-partitioning: when and When, it means and On road sections with the same longitudinal slope and free flow velocity, and... and The road segments are merged to obtain the merged road segment dataset. , This represents the total number of road segments after the merger. Let m be the m-th merged road segment, and , Let be the starting arc length coordinates of the m-th merged road segment. Let the coordinates be the arc length coordinates of the endpoint of the m-th merged road segment. Let m be the free-flow velocity of the merged road segment. Let be the longitudinal slope of the m-th merged road segment.

4. The method for estimating road capacity and bottleneck status considering the influence of longitudinal slope according to claim 3, characterized in that, Step 5 is performed as follows: The m-th merged road segment is calculated using equation (6). Traffic capacity This determines the traffic capacity of each section of the target road. ; (6) In equation (6), for Traffic density in free-flow state, obtained from equation (7); (7) In equation (7), for The optimal average headway is obtained from equation (8); (8) In equation (8), For the driver's reaction time, This is the minimum safe distance between vehicles; for The vehicle braking deceleration affected by the longitudinal slope is calculated using equation (9); (9) In equation (9), The vehicle braking deceleration when the longitudinal slope is 0; It represents the acceleration due to gravity.

5. The method for estimating road capacity and bottleneck status considering the influence of longitudinal slope according to claim 4, characterized in that, Step 6 is performed as follows: Step 6.1: Collect data on each segment of the target road at the current time. Traffic ;in, Indicates the first The merged road section Traffic; Define the bottleneck status of each segment of the target road. ,in, Indicates the first The merged road section Bottleneck state; initialization ; Step 6.2 Set the maximum update duration to ; Step 6.3: Obtain information using roadside equipment. Merged road segment dataset within the time period import flow , For the interval of traffic statistics; Step 6.4, Judgment If the condition is met, it means there is no traffic bottleneck on the target road, and proceed to step 6.5; otherwise, it means there is a traffic bottleneck on the target road, and proceed to step 6.

6. Step 6.5: According to equation (10), the first... The merged road section At any moment Update traffic Thus, the target road at time... Update traffic ,make Then, proceed to step 6.11; (10) Step 6.6: Initialize variables ; Step 6.7, determine the first The merged road section Traffic Does it hold true? If it does, it means that the first... The merged road section No traffic bottlenecks occurred, and the update was completed. The merged road section bottleneck status Proceed to step 6.8; otherwise, it indicates... Traffic bottlenecks occurred, and updates were made. Proceed to step 6.10; where, Indicates the first The merged road section Traffic capacity; Step 6.8, determine the first The merged road section Traffic Does it hold true? If it does, it means that the first... The merged road section No traffic bottleneck occurred, and the result of equation (11) was obtained. The merged road section At any moment Update traffic Proceed to step 6.10; otherwise, it indicates that the first step is... The merged road section A traffic bottleneck occurs, and the updated flow rate is obtained using equation (12). Proceed to step 6.10, where, Indicates the first The merged road section Traffic capacity; (11) (12) Step 6.9, Judgment Is it valid? If so, what is the road segment? No traffic bottleneck occurred, and the updated flow rate was obtained using equation (13). Proceed to step 6.10; otherwise, indicate the numbered road segment. and road sections Traffic bottlenecks occurred in all cases, and the updated flow rate was obtained using equation (14). Proceed to step 6.10; (13) (14) Step 6.10, Judgment Does it hold true? If it does, it means that we have obtained... Bottleneck status after constant updates and Merged road segment datasets under different time periods Update traffic Output the result and proceed to step 6.11; otherwise, output the result. Assign to Proceed to step 6.7 and execute sequentially; Step 6.11, Judgment If true, Assign to , Assign to Proceed to step 6.3 for sequential execution; otherwise, it indicates that... All bottleneck statuses and update traffic during the time period.

6. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store programs that support the processor in executing any of the road capacity and bottleneck state estimation methods considering the influence of longitudinal slope as described in claims 1-5, and the processor is configured to execute the programs stored in the memory.

7. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is run by the processor, it executes any one of the road capacity and bottleneck state estimation methods according to claims 1-5, which take into account the influence of longitudinal slope.

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