Traffic bottleneck identification method based on urban road network base and gate data

Abstract

The invention discloses a traffic bottleneck identification method based on urban road network base and bayonet data, and relates to the technical field of traffic monitoring. Accurate association of data and road sections is realized by building an urban road network base and configuring and binding bayonet equipment; acquiring long-term historical checkpoint data, and counting vehicle passing data volume and the proportion of large vehicles to small vehicles; based on the historical checkpoint data, calculating road section traffic capacity and theoretical traffic time by adopting a preset formula, and jointly taking the road section traffic capacity and theoretical traffic time as road section design capacity data; counting vehicle intersection average queuing time, road section average passing time and road section vehicle occupancy at preset time intervals, and completing traffic bottleneck monitoring index counting; based on road section design capability data and traffic bottleneck monitoring indexes, potential, short-term and long-term traffic bottlenecks are judged, congestion chains are tracked through a road network topological relation to identify cascade propagation attributes, and traffic bottleneck generation reasons are analyzed. According to the method, the traffic bottleneck can be quickly and accurately identified, and the cause of the traffic bottleneck is analyzed from multiple dimensions.

Description

Technical Field

[0001] This invention relates to the field of traffic monitoring technology, and in particular to a method for identifying traffic bottlenecks based on urban road network infrastructure and checkpoint data. Background Technology

[0002] Against the backdrop of rapid urbanization, the number of motor vehicles is growing exponentially. A significant imbalance exists between the designed capacity of existing road infrastructure and current traffic demand, leading to increasingly prominent structural traffic bottlenecks. Due to the non-linear aggregation characteristics of traffic flow during peak hours and the cascading propagation of bottleneck effects, traditional manual inspection methods struggle to accurately identify and spatially locate congestion sources, resulting in inefficient traffic management.

[0003] Traditional technologies primarily identify traffic bottlenecks through manual inspections and basic electronic equipment. Manual methods rely on on-site observation and experience-based judgment (such as traffic police recording traffic flow data), but are inefficient and highly subjective. Fixed detectors (loop coils, microwave radar) and video surveillance can collect traffic flow data, but their coverage is limited and they cannot identify dynamic bottlenecks. Sampling surveys and simple prediction models suffer from data lag and insufficient accuracy. Furthermore, due to the cascading propagation characteristics of bottlenecks, traditional methods cannot accurately pinpoint the key factors influencing their occurrence. Summary of the Invention

[0004] In view of this, this application provides a traffic bottleneck identification method based on urban road network base and checkpoint data to address the shortcomings of existing technologies.

[0005] The first aspect of this application provides a traffic bottleneck identification method based on urban road network infrastructure and checkpoint data, including: A city road network base is built using 3D road network editing and design software. The city road network base restores the road outline, number of lanes, lane speed limits, intersection lane turning connections, and traffic light timing relationships. Based on the basic information of the checkpoint equipment, checkpoint equipment is configured in the city road network base. At the same time, the checkpoint equipment is bound to the corresponding road segment and lane, so that the checkpoint data is accurately associated with the specific road segment and lane. Acquire historical checkpoint data corresponding to checkpoint devices within a set historical time period, and calculate the average number of vehicles passing through each checkpoint device per hour from 0 to 24 hours per day, as well as the ratio of small vehicles to large vehicles. Based on the historical checkpoint data bound to the road segment, the road segment's traffic capacity value and theoretical travel time are calculated using a preset formula, which together serve as the road segment's design capacity data; the theoretical travel time includes the theoretical travel time of road segments without traffic lights and the theoretical travel time of road segments with traffic lights. At preset intervals, the average queuing time, average travel time, and vehicle occupancy of vehicles in each direction at each road section checkpoint are statistically analyzed to complete the traffic bottleneck monitoring indicators. Based on the calculated road segment design capacity data and the statistical traffic bottleneck monitoring indicators, we make judgments on potential traffic bottlenecks, short-term traffic bottlenecks, and long-term traffic bottlenecks through comparative analysis. We also track congestion chains by combining road network topology relationships, identify the cascading propagation attributes of traffic bottlenecks, and analyze the causes of traffic bottlenecks.

[0006] In one possible implementation of the first aspect, the formula for calculating the road segment capacity value is as follows: This represents the road segment's traffic capacity, corresponding to the hourly traffic volume. This represents the theoretical capacity of a single lane, which is affected by lane speed limits. Number of lanes; This refers to the lane width coefficient; The influence coefficient for large vehicles is obtained based on the ratio of small vehicles to large vehicles. This is the driver coefficient and is the default value.

[0007] In one possible implementation of the first aspect, if the end point of the road segment is a traffic light intersection, the formula for calculating the capacity value of the corresponding intersection is as follows: This represents the traffic capacity value of the intersection corresponding to the road segment, and the corresponding hourly traffic flow. The duration of the complete signal cycle of the intersection traffic lights; This is the phase number, used to distinguish different phases within a complete signal cycle; The total number of independent phases contained in a complete signal cycle; For the first The green light time for each phase; The saturation locomotive headway is expressed in seconds. The lane function reduction factor is obtained using the following formula: , This refers to the number of left-turn lanes.

[0008] In one possible implementation of the first aspect, if the road segment is without traffic lights, the formula for calculating the theoretical travel time is as follows: The theoretical travel time for unlit road sections is in seconds. The length of the road segment; Design speed, in kilometers per hour; This is the theoretical distance between the front ends of the train, in meters. The standard headway is expressed in seconds.

[0009] In one possible implementation of the first aspect, if the road segment is a traffic light segment, the formula for calculating the theoretical travel time is as follows: The theoretical travel time for traffic-lit road sections is in seconds. The length of the road segment; Design speed, in kilometers per hour; Standard headway, in seconds; This is the lane width correction factor; This is the speed reduction factor for large vehicles; The dynamic headway is obtained by: The unit is meters; Standard headway, in seconds; The time-distance correction factor for large vehicles is obtained as follows: , The proportion of large vehicles; The signal delay time is obtained by... , The duration of a complete signal cycle for the intersection traffic lights. This represents the proportion of the green light time for the corresponding direction to the total signal cycle.

[0010] In one possible implementation of the first aspect, completing the statistical analysis of traffic bottleneck monitoring indicators includes: The average queuing time at the intersection is the time difference between the first and last time a vehicle is photographed by the checkpoint equipment. The average travel time of the road segment is the time difference between the last time a vehicle was photographed by the previous checkpoint device and the last time it was photographed by the checkpoint device in this road segment; The vehicle occupancy of the road segment is calculated by adding 1 when a vehicle enters the road segment as captured by the previous checkpoint device and subtracting 1 when a vehicle leaves the road segment as captured by the current checkpoint device.

[0011] In one possible implementation of the first aspect, potential traffic bottlenecks are identified, specifically as follows: In road sections with traffic lights, if the capacity value of the corresponding road section is less than the capacity value of the intersection at the end of the road section by a set ratio, the corresponding road section is determined to be a potential traffic bottleneck affected by traffic lights.

[0012] In one possible implementation of the first aspect, the short-term traffic bottleneck is determined as follows: If the average travel time of a road segment within the preset time period exceeds the first proportion of the corresponding theoretical travel time, it is determined to be traffic congestion. At intersections without traffic lights, if the average queuing time of vehicles exceeds a set time, it is determined that traffic in the corresponding direction is obstructed; at intersections with traffic lights, if the average queuing time of vehicles exceeds the second proportion value of a complete signal cycle, it is determined that traffic in the corresponding direction is obstructed. Based on the road network topology, congestion data of connecting road segments to obstructed traffic are found to form a complete congestion chain. The road segment in the congestion chain where traffic obstruction occurs earliest is the primary traffic bottleneck, and the remaining road segments are secondary traffic bottlenecks.

[0013] In one possible implementation of the first aspect, the causes of short-term traffic bottlenecks are analyzed and found to include: If it is a potential traffic bottleneck, it is determined that the traffic obstruction is caused by the timing of the traffic light phases. If it occurs within a specified time period, it is determined that traffic obstruction is caused by traffic flow. If the number of vehicles on a road segment is greater than the third proportion of the road's designed occupancy, it is determined that the road's designed number of lanes is causing traffic obstruction. If the number of vehicles on a road segment is less than the fourth proportion of the road's designed occupancy, it is determined that traffic obstruction is caused by illegal traffic behavior.

[0014] In one possible implementation of the first aspect, the long-term traffic bottleneck is assessed, specifically as follows: Within a set time period, the number of times traffic bottlenecks occur on each road segment is counted. The number of times a major traffic bottleneck occurs is counted is 1, and the number of times a secondary traffic bottleneck occurs is 0.5. The frequency of traffic bottlenecks on road sections and the frequency of their corresponding causes are sorted to obtain the sorting results; Based on the sorting results, road sections with high-frequency traffic bottlenecks and the corresponding causes of high frequency are identified.

[0015] Its beneficial effects are as follows: This invention discloses a traffic bottleneck identification method based on urban road network base and checkpoint data. It constructs an urban road network base that restores the physical and traffic attributes of roads using 3D road network editing and design software, configures and binds checkpoint equipment to achieve precise data-road segment correlation; acquires long-term historical checkpoint data, and statistically analyzes the volume of passing vehicles and the ratio of large vehicles to small vehicles; based on the historical checkpoint data, it calculates the road segment's capacity and theoretical travel time using a preset formula, which together serve as the road segment's design capacity data; at preset intervals, it statistically analyzes the average queuing time at intersections, the average travel time of road segments, and the number of vehicles occupying road segments, completing the statistical analysis of traffic bottleneck monitoring indicators; based on the road segment design capacity data and traffic bottleneck monitoring indicators, it identifies potential, short-term, and long-term traffic bottlenecks, traces congestion chains through road network topology relationships to identify cascading propagation attributes, and analyzes the causes of traffic bottlenecks. This invention effectively solves the problems of low efficiency, data lag, difficulty in identifying dynamic bottlenecks, and inability to track cascading propagation attributes in traditional manual inspections by deeply integrating urban road network infrastructure and checkpoint data. It can accurately identify potential, short-term, and long-term traffic bottlenecks, clearly locate the cascading propagation path and core causes of bottlenecks, and provide scientific data support for urban road optimization, traffic light timing adjustment, and traffic control decisions, significantly improving the pertinence and efficiency of traffic bottleneck management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of a traffic bottleneck identification method based on urban road network base and checkpoint data provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0020] Example In existing technologies, traffic bottlenecks are mainly identified through manual inspections and basic electronic equipment. However, these methods suffer from several problems: traffic police or engineers observe and record indicators such as traffic flow speed and queue length at fixed points (e.g., during peak hours) and combine this with historical experience to determine the location of bottlenecks. At traffic accident scenes, manual measurements are used to draw diagrams, which is time-consuming and has a high error rate. Manual origin-destination (OD) surveys or license plate recognition are used to statistically analyze travel patterns, but the data update cycle is long, resulting in data lag and insufficient accuracy. Fixed detectors (loop coils, microwave radar) and video surveillance can collect traffic flow data, but their coverage is limited and they cannot identify dynamic bottlenecks. Traditional traffic bottleneck identification methods do not consider the multi-layered causes of traffic bottlenecks in the context of urban road networks, and therefore lack good decision support for optimizing traffic bottlenecks. Due to the cascading propagation characteristics of bottlenecks, traditional methods cannot accurately locate the key factors influencing the occurrence of traffic bottlenecks.

[0021] Therefore, this application provides a traffic bottleneck identification method based on urban road network infrastructure and checkpoint data, such as... Figure 1 As shown, it includes: A city road network base is built using 3D road network editing and design software. The city road network base restores the road outline, number of lanes, lane speed limits, intersection lane turning connections, and traffic light timing relationships. Based on the basic information of the checkpoint equipment, checkpoint equipment is configured in the city road network base. At the same time, the checkpoint equipment is bound to the corresponding road segment and lane, so that the checkpoint data is accurately associated with the specific road segment and lane. Acquire historical checkpoint data corresponding to checkpoint devices within a set historical time period, and calculate the average number of vehicles passing through each checkpoint device per hour from 0 to 24 hours per day, as well as the ratio of small vehicles to large vehicles. Based on historical checkpoint data bound to road segments, the road segment's traffic capacity value and theoretical travel time, as well as the road segment's design capacity data, are calculated using a preset formula; the theoretical travel time includes the theoretical travel time for road segments without traffic lights and the theoretical travel time for road segments with traffic lights. At preset intervals, the average queuing time, average travel time, and vehicle occupancy of vehicles in each direction at each road section checkpoint are statistically analyzed to complete the traffic bottleneck monitoring indicators. Based on the calculated road segment design capacity data and the statistical traffic bottleneck monitoring indicators, we make judgments on potential traffic bottlenecks, short-term traffic bottlenecks, and long-term traffic bottlenecks through comparative analysis. We also track congestion chains by combining road network topology relationships, identify the cascading propagation attributes of traffic bottlenecks, and analyze the causes of traffic bottlenecks.

[0022] This embodiment provides a traffic bottleneck identification method based on urban road network infrastructure and checkpoint data. It includes basic environment setup, historical checkpoint data analysis and statistics, road design capacity calculation, traffic bottleneck monitoring index statistics, and traffic bottleneck judgment and analysis. It follows a progressive logic of establishing a baseline, collecting data, quantifying potential, capturing status, and determining the bottleneck. The principle of each step is designed to address the pain points of traditional technologies, and the formula construction is based on engineering practice principles and traffic flow theory, as detailed below: Basic environment setup (building a digital benchmark for precise data-spatial correlation): To address the pain points of traditional methods, such as data disconnect from roads and ambiguous positioning, a digital road network base is built and bound to checkpoint equipment. This establishes a correspondence between physical roads, digital models, and data acquisition terminals, providing a unified spatial benchmark and data association foundation for all subsequent analyses.

[0023] The construction of the urban road network base uses 3D road network editing software to recreate the core attributes of roads (outline, number / width / speed limit, intersection turning, signal timing), essentially digitally replicating the physical roads. Traditional methods rely solely on single traffic flow data without considering road design attributes, leading to inaccurate capacity calculations. The digital base, however, provides a quantitative foundation for the road design potential, making subsequent road capacity calculations more closely aligned with actual road conditions.

[0024] The configuration and binding of checkpoint equipment involves entering basic checkpoint information (number, location, orientation, and shooting range) and binding it to road segments / lanes. The core is to ensure that every piece of data collected by the checkpoint can be accurately associated with a specific road. Traditional fixed detectors have limited coverage and their data lacks clear spatial attribution. After binding, precise positioning of traffic data for specific lanes / road segments can be achieved, providing a prerequisite for subsequent bottleneck location identification.

[0025] Historical checkpoint data analysis and statistics (extracting basic parameters reflecting traffic patterns): To address the shortcomings of traditional methods, such as data lag and insufficient sample size, this method uses long-term historical checkpoint data to obtain representative basic parameters of traffic flow, providing data input for quantifying road design capabilities. Traffic flow exhibits temporal regularity (such as peak-hour characteristics) and vehicle type differences (small cars and large cars have different interference), requiring statistical extraction of key influencing factors.

[0026] When selecting the statistical period, choosing a long-term period of more than 3 months as the set historical period can cover traffic characteristics of different dates and times, avoid parameter distortion caused by short-term abnormal data, and ensure that the statistical results can reflect the normal traffic patterns of the road.

[0027] The core statistical indicators are designed based on the average daily vehicle traffic volume from 0 to 24 hours, which directly reflects the traffic demand intensity of the road segment and forms the basis for comparing the actual load of the road with its design capacity. The ratio of small vehicles to large vehicles is also important. Large vehicles are large, slow to accelerate, and have a long following distance, which significantly interferes with traffic efficiency and is a key factor affecting road capacity. The influence coefficient of large vehicles in subsequent formulas needs to be calculated based on this ratio, so it must be statistically analyzed separately.

[0028] Road design capacity calculation (quantifying the actual traffic potential of a road): To address the shortcomings of traditional methods that calculate capacity in a general way and fail to consider the impact of multiple scenarios, this paper constructs multi-scenario formulas based on traffic flow theory and engineering practice. It accurately quantifies the theoretical capacity of a road (maximum traffic volume per unit time) and the theoretical travel time (travel time under normal conditions), forming a benchmark threshold for judging traffic bottlenecks. If the actual traffic conditions exceed this benchmark, a traffic bottleneck may exist.

[0029] The formula for road segment capacity is as follows: The formula is constructed by starting from the theoretical traffic potential of a single lane, and then adding actual influencing factors such as the number of lanes, road physical conditions, traffic composition, and driving behavior to obtain the actual traffic capacity of the road segment. This is the theoretical capacity value for a single lane, which is affected by lane speed limits and is based on the design scope of urban roads. It represents the ideal traffic potential without considering any interference. The more lanes there are, the greater the traffic capacity, which is the basic law of linear superposition of load traffic flow. This is the lane width coefficient. Lane width affects the lateral safety distance of vehicles. Vehicles can drive normally when the standard is 3.5 meters. Insufficient width will lead to increased lateral interference and reduced traffic efficiency (0.9 for 3.0-3.5 meters, 0.85 for less than 3.0 meters). The factor representing the impact of large vehicles is used because large vehicles occupy more road space and interfere with the driving of vehicles behind them. The higher the proportion of small vehicles, the less interference from large vehicles. Therefore, the decimal form of the proportion of small vehicles is used, such as 80%. The value of 0.8 aligns with the linear impact of vehicle type composition on traffic efficiency in engineering practice; This is the driver coefficient and its default value, which is 0.95. It takes into account the slight decrease in traffic efficiency caused by differences in driver operation in actual driving and is an empirical correction coefficient in engineering.

[0030] The formula for the traffic capacity of a traffic-lit intersection is as follows: The formula's framework logic is that intersections adopt multi-phase traffic release (such as east-west and north-south traffic release respectively), and it is necessary to calculate and superimpose the traffic capacity of each phase, with the core considerations being effective release time and lane function limitations.

[0031] (Multi-phase summation) Each phase of the intersection is allowed to pass independently, and the total capacity is the sum of the capacities of all phases, which conforms to the superposition law of multi-phase traffic flow; (Maximum traffic volume of a lane per unit time). (Saturation headway, taken as 2.4 seconds) is the minimum safe time interval for continuous traffic, divided by 1 per hour. This yields the maximum traffic volume under ideal conditions for a single lane. (Phase green light ratio) (No. (Green time for each phase) and The ratio of (signal cycle) reflects the proportion of effective passage time for that phase. The longer the green light time, the more effective passage time and the stronger the passage capacity. The lane function reduction factor is obtained using the following formula: , This refers to the number of left-turn lanes. Left-turn lanes can conflict with oncoming straight-through traffic, reducing traffic efficiency. Therefore, for each additional left-turn lane, the reduction factor is reduced by 0.1 to align with the impact of intersection lane functions on traffic flow.

[0032] The formula for the theoretical travel time on a road section without traffic lights is as follows: The formula is based on the logic that theoretical travel time = basic travel time + additional time for vehicles to follow each other, without considering traffic light interference, and is consistent with the characteristics of continuous travel on unsignaled roads.

[0033] (Basic driving time): (Road segment length) divided by The time taken to travel at the design speed in hours is calculated by multiplying the design speed by 3600 and converting it to seconds. (Additional time for following the car) (Theoretical number of vehicles in the road section) multiplied by (Standard headway, taken as 2 seconds) reflects the following waiting time when multiple vehicles are driving continuously. The longer the road segment and the denser the vehicles, the longer the following time, which is consistent with actual driving scenarios.

[0034] The theoretical travel time formula for traffic light sections is as follows: The formula is built upon the formula for roads without traffic lights by adding corrections for the impact of large vehicles and the time of signal delay, thus reflecting the actual characteristics of traffic light interference and waiting at traffic lights on roads with traffic lights.

[0035] (Lane width correction factor) (Large vehicle speed reduction factor) corrects for the impact of lane width and large vehicles on actual driving speed (for every 10% increase in the proportion of large vehicles, the speed decreases by 8%), making the basic driving time more accurate; (Dynamic frontage spacing): The faster the vehicle speed, the greater the safe frontage spacing, which is more in line with actual driving patterns than a fixed frontage spacing. (Time difference correction factor for large vehicles) Large vehicles are longer, so the following time needs to be increased. The higher the proportion of large vehicles, the larger the correction coefficient, reflecting the amplified impact of large vehicles on car-following time; (Signal delay time), the average waiting time caused by traffic lights. The smaller the percentage of green time, the longer the delay, which aligns with the actual scenario of waiting at a red light. It is a simplified calculation of average delay in engineering, which conforms to the waiting pattern within the signal period.

[0036] Traffic bottleneck monitoring indicators statistics (real-time capture of actual traffic operation status): To address the shortcomings of traditional traffic condition monitoring methods, such as limited dimensions and poor real-time performance, this method uses real-time data from checkpoints to statistically analyze three core indicators that reflect congestion levels, traffic efficiency, and road segment load. This provides real-time data support for comparing subsequent benchmark design capabilities with actual monitoring indicators.

[0037] The statistical period design, which selects half-hourly statistics, avoids data redundancy caused by short periods such as 10 minutes, while also capturing short-term traffic fluctuations (such as sudden congestion) in a timely manner. This balances real-time performance with data validity and aligns with the characteristics of short-term changes and long-term patterns in urban traffic.

[0038] The statistical logic of the three major indicators: The average queuing time at intersections is calculated based on the time difference between the first and last time a vehicle is photographed by the checkpoint. Since the checkpoint can accurately record the duration of a vehicle's stay at the intersection, the queuing time directly reflects the degree of traffic obstruction at the intersection. The average travel time of a road segment is calculated from the time difference between the last photo taken at the previous checkpoint and the last photo taken at the current checkpoint. It directly reflects the actual traffic efficiency of vehicles within the road segment and is a core indicator for judging congestion (the longer the travel time, the more severe the congestion). The vehicle occupancy of a road segment is dynamically counted by entering (+1) and leaving (-1), reflecting the vehicle load within the segment in real time. The higher the load, the closer the road is to saturation, and the more likely it is to form a bottleneck.

[0039] Traffic bottleneck identification and analysis (comparing benchmarks with actual conditions to accurately pinpoint bottlenecks and their causes): To address the shortcomings of traditional methods, such as vague bottleneck location, singular cause analysis, and failure to consider cascading propagation, this method compares design capabilities with monitoring indicators and combines them with road network topology to achieve three levels of bottleneck identification: potential, short-term, and long-term. At the same time, it accurately traces the causes based on scene characteristics.

[0040] Potential traffic bottleneck assessment: The principle is that the traffic capacity of the road segment and the intersection is mismatched. When the traffic capacity of the road segment is less than 20% of the traffic capacity of the intersection, it indicates that the timing of the traffic lights at the intersection is unreasonable (such as the green light time is too short), which makes it impossible for the traffic flow on the road segment to pass through the intersection quickly, forming a potential bottleneck. This fits the engineering scenario where traffic capacity is unbalanced due to signal control.

[0041] Short-term traffic bottleneck assessment: The principle is that the actual operating conditions exceed the theoretical benchmark, as follows: Congestion is determined when the actual travel time of a road segment exceeds 40% of the theoretical travel time. This 40% deviation is the engineering threshold that distinguishes between normal fluctuations and congestion. Exceeding this threshold indicates a significant decrease in road operating efficiency. Traffic obstruction is determined by whether the queue at an intersection without traffic lights exceeds 1 minute or the queue at an intersection with traffic lights exceeds 30% of the signal cycle. This aligns with the actual control requirements of "no waiting at intersections without traffic lights" and "waiting at intersections with traffic lights should not exceed 1 / 3 of the signal cycle," respectively. Cascade propagation identification traces congestion chains based on road network topology. Because traffic bottlenecks have cascade propagation characteristics (e.g., congestion on road segment A can spread to upstream and downstream road segments B and C), chain analysis can pinpoint the source of congestion (the earliest congested road segment is the main bottleneck), overcoming the shortcomings of traditional methods that only look at single points and not the overall situation.

[0042] Long-term traffic bottleneck assessment: The principle is that high-frequency problems are the core problems: the number of times bottlenecks occur and their corresponding causes are counted on a monthly basis. Major traffic bottlenecks are counted as 1 time, and secondary traffic bottlenecks are counted as 0.5 times. By sorting, high-frequency bottleneck road sections and their causes are identified, which is in line with the actual need of traffic management to prioritize solving high-frequency problems.

[0043] Cause analysis logic: Based on the correspondence between scene features and causes: Potential traffic bottlenecks correspond to unreasonable traffic light timing (a direct cause of capacity mismatch). Congestion occurs during peak hours (such as 7:00 to 9:00 / 17:00 to 19:00), corresponding to excessive traffic volume (time period characteristics determine demand intensity). The vehicle occupancy rate of the road section is greater than 85% of the road's designed occupancy rate, and the corresponding number of lanes is insufficient (meeting the upper limit of the design, but the supply is insufficient). If the vehicle occupancy of a road segment is less than 60% of the road's designed occupancy, corresponding to traffic violations (such as illegal parking and traffic accidents), the load is low but traffic is obstructed, which is considered abnormal interference. The above classification logic covers the four core causes of traffic bottlenecks: signal control, demand intensity, road supply, and abnormal interference, enabling multi-dimensional and accurate tracing.

[0044] In some embodiments, the formula for calculating the traffic capacity value of the road segment is as follows: This represents the road segment's traffic capacity, corresponding to the hourly traffic volume. This represents the theoretical capacity of a single lane, which is affected by lane speed limits. Number of lanes; This refers to the lane width coefficient; The influence coefficient for large vehicles is obtained based on the ratio of small vehicles to large vehicles. This is the driver coefficient and is the default value.

[0045] In some embodiments, if the end of the road segment is a traffic-lit intersection, the formula for calculating the capacity value of the corresponding intersection is as follows: This represents the traffic capacity value of the intersection corresponding to the road segment, and the corresponding hourly traffic flow. The duration of the complete signal cycle of the intersection traffic lights; This is the phase number, used to distinguish different phases within a complete signal cycle; The total number of independent phases contained in a complete signal cycle; For the first The green light time for each phase; The saturation locomotive headway is expressed in seconds. The lane function reduction factor is obtained using the following formula: , This refers to the number of left-turn lanes.

[0046] In some embodiments, if the road segment is without traffic lights, the formula for calculating the theoretical travel time is as follows: The theoretical travel time for unlit road sections is in seconds. The length of the road segment; Design speed, in kilometers per hour; This is the theoretical distance between the front ends of the train, in meters. The standard headway is expressed in seconds.

[0047] In some embodiments, if the road segment is a traffic light segment, the formula for calculating the corresponding theoretical travel time is as follows: The theoretical travel time for traffic-lit road sections is in seconds. The length of the road segment; Design speed, in kilometers per hour; Standard headway, in seconds; This is the lane width correction factor; This is the speed reduction factor for large vehicles; The dynamic headway is obtained by: The unit is meters; Standard headway, in seconds; The time-distance correction factor for large vehicles is obtained as follows: , The proportion of large vehicles; The signal delay time is obtained by... , The duration of a complete signal cycle for the intersection traffic lights. This represents the proportion of the green light time for the corresponding direction to the total signal cycle.

[0048] In some embodiments, completing the statistical analysis of traffic bottleneck monitoring indicators includes: The average queuing time at the intersection is the time difference between the first and last time a vehicle is photographed by the checkpoint equipment. The average travel time of the road segment is the time difference between the last time a vehicle was photographed by the previous checkpoint device and the last time it was photographed by the checkpoint device in this road segment; The vehicle occupancy of the road segment is calculated by adding 1 when a vehicle enters the road segment as captured by the previous checkpoint device and subtracting 1 when a vehicle leaves the road segment as captured by the current checkpoint device.

[0049] In some embodiments, the potential traffic bottleneck is identified as follows: In road sections with traffic lights, if the capacity value of the corresponding road section is less than the capacity value of the intersection at the end of the road section by a set ratio, the corresponding road section is determined to be a potential traffic bottleneck affected by traffic lights.

[0050] In some embodiments, the determination of short-term traffic bottlenecks specifically involves: If the average travel time of a road segment within the preset time period exceeds the first proportion of the corresponding theoretical travel time, it is determined to be traffic congestion. At intersections without traffic lights, if the average queuing time of vehicles exceeds a set time, it is determined that traffic in the corresponding direction is obstructed; at intersections with traffic lights, if the average queuing time of vehicles exceeds the second proportion value of a complete signal cycle, it is determined that traffic in the corresponding direction is obstructed. Based on the road network topology, congestion data of connecting road segments to obstructed traffic are found to form a complete congestion chain. The road segment in the congestion chain where traffic obstruction occurs earliest is the primary traffic bottleneck, and the remaining road segments are secondary traffic bottlenecks.

[0051] In some embodiments, for short-term traffic bottlenecks, the causes of the traffic bottlenecks are analyzed and identified as follows: If it is a potential traffic bottleneck, it is determined that the traffic obstruction is caused by the timing of the traffic light phases. If it occurs within a specified time period, it is determined that traffic obstruction is caused by traffic flow. If the number of vehicles on a road segment is greater than the third proportion of the road's designed occupancy, it is determined that the road's designed number of lanes is causing traffic obstruction. If the number of vehicles on a road segment is less than the fourth proportion of the road's designed occupancy, it is determined that traffic obstruction is caused by illegal traffic behavior.

[0052] In some embodiments, the determination of long-term traffic bottlenecks is specifically as follows: Within a set time period, the number of times traffic bottlenecks occur on each road segment is counted. The number of times a major traffic bottleneck occurs is counted is 1, and the number of times a secondary traffic bottleneck occurs is 0.5. The frequency of traffic bottlenecks on road sections and the frequency of their corresponding causes are sorted to obtain the sorting results; Based on the sorting results, road sections with high-frequency traffic bottlenecks and the corresponding causes of high frequency are identified.

[0053] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computing software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0054] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0055] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for traffic bottleneck identification based on urban road network base and pocket data, characterized in that, include: The city road network base is built using 3D road network editing and design software. The city road network base restores the road outline, number of lanes, lane speed limits, intersection lane turning connections and traffic light timing relationships. Based on the basic information of the checkpoint equipment, checkpoint equipment is configured in the urban road network base, and the checkpoint equipment is bound to the corresponding road segment and lane, so that the checkpoint data is accurately associated with the specific road segment and lane. Acquire historical checkpoint data corresponding to checkpoint devices within a set historical time period, and calculate the average number of vehicles passing through each checkpoint device per hour from 0 to 24 hours per day, as well as the ratio of small vehicles to large vehicles. Based on the historical checkpoint data bound to the road segment, the road segment's traffic capacity value and theoretical passage time are calculated using a preset formula, which together serve as the road segment's design capacity data. The theoretical travel time includes the theoretical travel time for road sections without traffic lights and the theoretical travel time for road sections with traffic lights; At preset intervals, the average queuing time, average travel time, and vehicle occupancy of vehicles in each direction at each road section checkpoint are statistically analyzed to complete the traffic bottleneck monitoring indicators. Based on the calculated road segment design capacity data and the statistical traffic bottleneck monitoring indicators, we make judgments on potential traffic bottlenecks, short-term traffic bottlenecks, and long-term traffic bottlenecks through comparative analysis. We also track congestion chains by combining road network topology relationships, identify the cascading propagation attributes of traffic bottlenecks, and analyze the causes of traffic bottlenecks. 2.The traffic bottleneck identification method based on urban road network base and chink data according to claim 1, wherein, The formula for calculating the traffic capacity value of the road section is as follows: is the road segment capacity value, corresponding to the hourly traffic volume; is the single lane theoretical capacity value, affected by the lane speed limit; is the number of lanes; is the lane width coefficient; is the large vehicle impact coefficient, based on the small and large vehicle proportion data; is the driver coefficient and is the default value. 3.The traffic bottleneck identification method based on urban road network base and chink data according to claim 2, characterized in that, If the end of the road segment is a traffic-lit intersection, the formula for calculating the capacity value of the corresponding intersection is as follows: is the capacity value of the intersection corresponding to the link, corresponding to the hourly traffic volume; is the length of the complete signal cycle of the intersection; is the phase number, used to distinguish different phases in a complete signal cycle; is the total number of independent phases contained in a complete signal cycle; is the green time of the phase; is the saturated headway, in seconds; is the lane function reduction coefficient, and the formula is , is the number of left-turn lanes. 4.The traffic bottleneck identification method based on urban road network base and chink data according to claim 1, wherein, If the road segment has no traffic lights, the formula for calculating the theoretical travel time is as follows: is the theoretical travel time for the section without signal, in seconds; is the length of the section; is the design speed, in km / h; is the theoretical headway, in meters; is the standard headway, in seconds.

5. The method of claim 1, wherein, If the road segment has traffic lights, the formula for calculating the theoretical travel time is as follows: is the theoretical travel time of the road section with signal, in seconds; is the length of the road section; is the design speed, in km / h; is the standard headway, in seconds; is the lane width correction factor; is the large vehicle speed reduction factor; is the dynamic headway, obtained by , in meters; is the standard headway, in seconds; is the large vehicle headway correction factor, obtained by , is the proportion of large vehicles; is the signal delay time, obtained by , is the complete signal cycle time of the intersection signal, is the proportion of green light time of the corresponding direction to the complete signal cycle.

6. The method of claim 1, wherein, The completion of traffic bottleneck monitoring indicators statistics includes: The average queuing time at the intersection is the time difference between the first and last time a vehicle is photographed by the checkpoint equipment. The average travel time of the road segment is the time difference between the last time a vehicle was photographed by the previous checkpoint device and the last time it was photographed by the checkpoint device in this road segment; The vehicle occupancy of the road segment is calculated by adding 1 when a vehicle enters the road segment as captured by the previous checkpoint device and subtracting 1 when a vehicle leaves the road segment as captured by the current checkpoint device.

7. The method of claim 3, wherein the method further comprises: The identification of potential traffic bottlenecks is specifically as follows: In road sections with traffic lights, if the capacity value of the corresponding road section is less than the capacity value of the intersection at the end of the road section by a set ratio, the corresponding road section is determined to be a potential traffic bottleneck affected by traffic lights. 8.The traffic bottleneck identification method based on urban road network base and pocket data according to claim 1, wherein, The assessment of short-term traffic bottlenecks is as follows: If the average travel time of a road segment within the preset time period exceeds the first proportion of the corresponding theoretical travel time, it is determined to be traffic congestion. At intersections without traffic lights, if the average queuing time of vehicles exceeds a set time, it is determined that traffic in the corresponding direction is obstructed; at intersections with traffic lights, if the average queuing time of vehicles exceeds the second proportion value of a complete signal cycle, it is determined that traffic in the corresponding direction is obstructed. Based on the road network topology relationship, congestion data of a road section connected with the road section where traffic is blocked is searched to form a complete congestion chain, and a road section where traffic is blocked earliest in time is a main traffic bottleneck and the remaining road sections are secondary traffic bottlenecks. 9.The traffic bottleneck identification method based on urban road network base and chink data according to claim 8, characterized in that, For a short-term traffic bottleneck, the following reasons are analyzed to cause the traffic bottleneck: If the traffic bottleneck is potential, it is determined that signal phase timing causes traffic to be blocked; If the traffic bottleneck occurs in a set time period, it is determined that traffic volume causes traffic to be blocked; If the vehicle occupancy of the road section is greater than a third proportion value of the road design occupancy, it is determined that the number of road design lanes causes traffic to be blocked; If the vehicle occupancy of the road section is less than a fourth proportion value of the road design occupancy, it is determined that traffic illegal behavior causes traffic to be blocked.

10. The method of claim 9, wherein the method further comprises: For a long-term traffic bottleneck, the following is determined: In a set time period, the number of times of the traffic bottleneck of each road section is counted, the number of times of the main traffic bottleneck is 1, and the number of times of the secondary traffic bottleneck is 0.5; The number of times of the traffic bottleneck of each road section and the number of times of the corresponding causes are sorted to obtain a sorting result; Based on the sorting result, a road section with a high-frequency traffic bottleneck and a corresponding high-frequency cause are determined.

Images