Method, device and equipment for analyzing influence of traffic event on toll and medium

Abstract

The invention discloses a method, a device and equipment for analyzing the influence of a traffic event on toll, and a medium. Comprising the following steps: determining a plurality of OD pairs influenced by a traffic event based on an OD path set and a road section where the traffic event occurs; determining a time range influenced by the traffic event based on the occurrence time and the end time of the traffic event; based on the time range, counting the traffic volume corresponding to the target vehicle model on each path between each pair of ODs, and obtaining the change of the path traffic volume between the ODs compared with the difference of a reference value; and analyzing the change of the toll between the ODs based on the change of the path traffic volume between the ODs. According to the scheme provided by the invention, the influence of the traffic event on the highway toll is analyzed based on group behavior statistics such as the inter-OD path traffic volume, the analysis efficiency and accuracy are improved, the random error of individual behavior estimation is reduced, and a data basis is provided for related departments to optimize highway operation and operation services.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and more specifically, to a method, device, equipment and medium for analyzing the impact of traffic events on tolls. Background Art

[0002] The core operating goal of highway operating entities is to ensure the normal collection of tolls, which is not only related to the operating activities of highways (such as differentiated charging), but also closely related to the operating conditions of highways. When abnormal operating events such as large traffic, traffic accidents, and traffic control occur on highways, large-scale congestion or restrictions on vehicle behavior will occur. In this case, car owners can timely learn about road conditions and adjust travel behavior through dynamic traffic information services (for example, in the face of congestion or control, car owners can choose not to detour to queue, detour to other highways, or detour to ground roads, etc.), which will directly affect the toll revenue of different highway operators.

[0003] How to quantify the impact of traffic incidents on highway tolls, build the correlation between highway operation and management activities, and provide a data basis for optimizing highway operation and management are issues that the industry urgently needs to solve. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment and medium for analyzing the impact of traffic events on tolls, so as to at least solve the technical problem in the related art that it is difficult to quantify and analyze the impact of traffic events on tolls.

[0005] According to one aspect of an embodiment of the present application, a method for analyzing the impact of a traffic event on a toll is provided, comprising:

[0006] Based on the OD path set and the road section where the traffic event occurs, determining a plurality of OD pairs affected by the traffic event;

[0007] Determine the time range of the traffic event's impact based on the traffic event's occurrence and end times;

[0008] Based on the time range, the traffic volume corresponding to the target vehicle type on each path between each pair of ODs is counted, and the difference compared with the benchmark value is used to obtain the change of the traffic volume between the OD paths;

[0009] Based on the change in traffic volume between the OD paths, the change in toll between the OD paths is analyzed.

[0010] According to another aspect of the embodiment of the present application, there is also provided a device for analyzing the impact of a traffic event on a toll, comprising:

[0011] A first analysis module is used to determine a plurality of OD pairs affected by the traffic event based on the OD path set and the road section where the traffic event occurs;

[0012] A second analysis module is used to determine the time range of the traffic event based on the occurrence time and end time of the traffic event;

[0013] The third analysis module is used to count the traffic volume corresponding to the target vehicle type on each path between each pair of ODs based on the time range, and obtain the change of the traffic volume between the OD paths compared with the difference of the benchmark value;

[0014] The statistical module is used to analyze the change of toll between OD based on the change of traffic volume between OD paths.

[0015] According to another aspect of an embodiment of the present application, there is also provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-mentioned method for analyzing the impact of traffic events on tolls through the computer program.

[0016] According to another aspect of the embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for analyzing the impact of traffic events on tolls when running.

[0017] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0018] The embodiment of the present application determines the multiple OD pairs affected and the affected time range based on the road section where the traffic incident occurs, and counts the changes in the traffic volume between the OD paths corresponding to the target vehicle type on each path between each pair of ODs within the corresponding time range, and then analyzes the changes in tolls between ODs.

[0019] First, this method uses statistics based on group vehicle driving behavior as a measurement. Compared with the vehicle-based model, which requires estimation of individual vehicle behavior, it can significantly reduce the random error of the estimation and improve the accuracy of the analysis.

[0020] Secondly, it breaks the assumption in traditional models that vehicles strictly follow the shortest path. By comparing and analyzing the changes in traffic volume between OD paths on normal days and abnormal days, it can fully capture the diversity of vehicle behaviors when traffic incidents occur. This method can not only cover the various action paths that vehicles may take when facing traffic incidents, including detours, early or delayed entry to the highway, etc., but also quantify the specific impact of these behaviors on highway toll revenue.

[0021] Finally, this method can also quantify the contribution of tolls between different ODs, clarify the focus of operation guarantee, optimize resource allocation, and give priority to solving traffic incidents that lead to toll loss, thereby improving the operational efficiency and economic benefits of highways and providing a data basis for relevant departments to optimize highway operation and operation business. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 is a flow chart of a method for analyzing the impact of a traffic event on tolls according to an embodiment of the present application;

[0024] Figure 2 A quantitative analysis model of the impact of traffic events on toll collection from a vehicle perspective according to an embodiment of the present application;

[0025] Figure 3 It is a scene diagram of the correspondence between OD traffic volume changes and vehicle owner behaviors according to an embodiment of the present application;

[0026] Figure 4A is a schematic diagram of a normal day bus route selection ratio according to an embodiment of the present application;

[0027] Figure 4B is a schematic diagram of a truck route selection ratio on a normal day according to an embodiment of the present application;

[0028] Figure 4C is a schematic diagram of bus route selection ratio on an abnormal day according to an embodiment of the present application;

[0029] Figure 4D is a schematic diagram of a truck route selection ratio on an abnormal day according to an embodiment of the present application;

[0030] Figure 5 It is a quantitative analysis model of the impact of traffic events on toll collection from an OD perspective according to an embodiment of the present application;

[0031] Figure 6 is a benchmark knowledge base classification model according to an embodiment of the present application;

[0032] Figure 7 This is a schematic diagram of a scenario in which a traffic event affects tolls according to an embodiment of the present application;

[0033] Figure 8 is a schematic diagram of a device for analyzing the impact of traffic events on tolls according to an embodiment of the present application;

[0034] Fig. 9 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] When a traffic incident occurs on a highway, changes in the driver's travel behavior will have an impact on toll collection, including the scenarios shown in the following table:

[0038]

[0039] In some existing methods, Figure 2 The quantitative analysis model of the impact of traffic events on toll collection is shown in Figure . Figure 2 As shown in the figure, the analysis is based on the vehicle perspective. First, the paths of vehicles within the time and space range of the incident are restored. One path is the shortest path between their ODs, path1, and the other is the actual driving path between their ODs, path2; secondly, if the shortest path path1 of the vehicle passes through the section where the incident occurred, it is determined that the vehicle is affected by the abnormal event and chooses another path path2; finally, the difference in toll settlement between path1 and path2 for each highway operator is calculated. By adding up the difference in toll settlement for each vehicle within the time and space range of the incident, the quantitative impact of traffic abnormalities on toll collection is obtained.

[0040] The assumption of the above model is that the path selection of the vehicle is strictly shortest path priority. For most scenarios, the above assumption is valid. However, empirical data shows that if there are several paths with similar distances between ODs, the vehicle will choose different paths with certain preferences and proportions, and will not necessarily choose the path with the shortest distance, that is, it does not meet the strict shortest path priority. Secondly, scenarios such as vehicles leaving the highway early or not taking the highway are not perceived. The overall analysis set of vehicles is difficult to define, which will seriously affect the accuracy of the model analysis. Therefore, a new analysis model needs to be proposed.

[0041] First, let’s explain the concepts involved in this invention:

[0042] OD: A vehicle completes a transfer from point O to point D in a trip on the highway. O is the departure point and D is the arrival point. In this application, O and D are replaced by the entrance toll station and exit toll station of this trip, respectively. Further, define t o is the time when the vehicle enters the entrance toll station, t d It is the time when the vehicle leaves the exit toll booth.

[0043] OD traffic volume: The traffic volume that starts from point O and finally arrives at point D during the statistical period is called the OD traffic volume of the statistical period.

[0044] like Figure 3 The figure shows the correlation between traffic events and vehicle behavior based on OD traffic volume. When a traffic event occurs on a certain path between OD and OD, when scenario S2 occurs, the traffic volume between OD does not decrease, but the proportion of path selection between OD changes; when scenario S3 occurs, the traffic volume between OD decreases, and the traffic volume of OD1 and O1D increases; when scenario S4 occurs, the traffic volume between OD decreases, and the traffic volume of OD1 increases; when scenario S5 occurs, the traffic volume between OD decreases, and the traffic volume of O1D increases; when scenarios S6 and S7 occur, the traffic volume between OD decreases.

[0045] When a traffic incident occurs on a path between ODs, the reduction of traffic volume between ODs, the transfer of traffic volume between ODs between different paths, and the increase of traffic volume on sub-paths between ODs can cover all the scenarios in the table, thereby making scenarios S4 to S7 from imperceptible to perceptible.

[0046] This application unifies various scenarios into a unified measurement of OD traffic volume change, without the need to distinguish between scenarios and vehicles, which helps reduce the complexity of analyzing the impact of traffic events on highway toll collection.

[0047] This application introduces the concepts of path and path traffic.

[0048] Path: There are one or more accessible routes between point O and point D. The accessible route chosen by a vehicle to transfer from point O to point D in a high-speed trip is called the vehicle's path.

[0049] Path traffic volume: The number of trips that vehicles take on different accessible routes between ODs for the same OD during the statistical period. Like OD traffic volume, path traffic volume is based on the time t o Definition: The number of vehicle trips that start from point O and eventually arrive at point D during the statistical period and choose a certain path.

[0050] When a traffic incident occurs on a reachable route between OD and the vehicle detours to reach point D via other highways, it will cause changes in the traffic volume between OD.

[0051] The path traffic volume can also be indicated by the normalized path selection ratio. The path selection ratio refers to the proportion of vehicles choosing different accessible routes between ODs for the same OD during the statistical period, which is called the path selection ratio.

[0052] As shown in Figure 4, this application empirically studies the impact of a traffic incident on the path selection of different vehicle types with the same OD.

[0053] Figures 4(a) and (b) show the proportion of the shortest and second shortest paths selected by buses and trucks on normal days. It can be seen that the proportion of paths selected by buses and trucks on normal days is relatively stable, with buses selecting the shortest and second shortest paths at a ratio of about 4:6; trucks selecting the shortest and second shortest paths at a ratio of 8:2. Figures 4(c) and (d) show the proportion of the shortest and second shortest paths selected by buses and trucks on the day when an abnormal event occurs. The gray lines of the secondary coordinates in Figures 4(c) and (d) are the congestion mileage caused by abnormal events occurring on the shortest path at different times. It can be seen from Figures 4(c) and (d) that due to the occurrence of abnormal traffic events on the shortest path, more and more vehicles choose the second shortest path. The proportion of buses selecting the second shortest path increases from 60% on normal days to a maximum of about 90%, and there is no corresponding decline after the abnormal event ends; the proportion of trucks selecting the second shortest path increases from 20% on normal days to a maximum of about 70%, and gradually declines with the end of the abnormal event.

[0054] It can be seen from the empirical data that the path traffic volume (path selection ratio) measurement can well reflect the response of different vehicle models to traffic events under a single OD, and at the same time, it can also measure the behavioral differences between different vehicle models. At the same time, the difference in OD traffic volume of different vehicle models on normal days and abnormal days can be statistically analyzed. The combination of the two can quantitatively analyze the impact of abnormal traffic events on the toll settlement of different transportation operating entities under a single OD. Similarly, the impact of abnormal traffic events on the toll settlement of different transportation operating entities under any OD can be analyzed, and then the total impact of the abnormal traffic events on the toll collection of different transportation operating entities can be accumulated. Thus, the following is formed. Figure 5 As shown, the quantitative analysis model of the impact of abnormal traffic events on toll collection from the OD perspective is presented. Figure 5 The model in this paper analyzes the difference in toll revenue of operating entities based on the changes in traffic volume between OD paths.

[0055] The following is a detailed description of the method for analyzing the impact of traffic events on tolls in the embodiment of the present application in conjunction with the accompanying drawings. Figure 1 As shown, the method mainly includes the following steps:

[0056] S101 determines a plurality of OD pairs affected by a traffic event based on an OD path set and a road section where the traffic event occurs.

[0057] Before executing step S101, the charging data is also pre-processed.

[0058] First, the data definition is given.

[0059] For a given highway network G(N,E), where N is the set of all nodes in the network and E is the set of all road sections in the network, the topology of the highway network G can be constructed based on N and E, which will not be repeated here.

[0060] To study the impact of traffic incidents and highway tolls, we must first introduce sensors for highway toll services, including entrance toll stations, exit toll stations, and toll gantries on highways. Through the data collected by the above sensors, we can restore the vehicle's driving path on the highway, and then calculate the tolls that the vehicle needs to pay, as well as the toll amounts allocated to different highway management entities.

[0061] Assume fee_item is a high-speed fee record, defined by the following six-tuple:

[0062] fee_item=<travel_id,car_id,t,g_id,car_type,fee>

[0063] Among them, travel_id uniquely identifies a trip; car_id uniquely identifies a car; g_id uniquely identifies a toll sensor, which can be deployed at the entrance toll station, exit toll station or toll gate of the highway; t is the time when the vehicle passes the toll sensor; car_type records the vehicle model, because the payment base of different models is different; fee is the toll amount that the vehicle needs to pay at the toll sensor based on the vehicle model. Regardless of the case of missing toll records, the accumulation of all toll records including tolls for a vehicle trip is the highway toll that the vehicle needs to pay for that trip.

[0064] The collection of toll collection records collected for a trip identified by tid is recorded as fee_items(tid), which is defined as follows:

[0065] fee_items(tid)={fee_item|fee_item.travel_id=tid}

[0066] Among them, all the fee records in the fee_items(tid) set are sorted according to fee_item.t. The g_id field of the first record identifies the entrance toll station, recorded as O(tid), and the t field of the first record identifies the time when the vehicle enters the highway, recorded as BgnTime(tid); the g_id field of the last record identifies the exit toll station, recorded as D(tid), and the t field of the last record identifies the time when the vehicle leaves the highway, recorded as EndTime(tid); the other fields identify the other toll gantries that the vehicle passes through during the journey in order.

[0067] Based on the g_id field of any fee record fee_item contained in the fee_items(tid) set, query the geographic coordinates of the toll sensor corresponding to g_id and map it to the highway network G, which is equivalent to obtaining the sequence of waypoints of the trip identified by pid. Perform a path restoration operation on the highway network G, that is, find a reachable path from O(tid) to D(tid) under the constraint of the waypoint sequence. The generated result is recorded as path(tid), which is a sequence set of road sections, recorded as:

[0068]

[0069] Where K is the number of road segments contained in the path. is the first road segment in the path(tid) sequence, which is the road segment where O(tid) is located in the road network G; It is the last road segment in the path(tid) sequence and is the road segment where D(tid) is located in the road network G.

[0070] For a given highway toll management entity (identified by m_id), if a toll sensor belongs to the entity, the toll collected by the vehicle at the toll sensor is included in the revenue of the current highway toll management entity. Then the toll revenue of a trip identified by tid to the highway toll management entity identified by m_id is:

[0071]

[0072] in,

[0073]

[0074] In one embodiment of the present application, the toll records within a preset analysis period are divided into data according to the trip identifier, and the toll record set and vehicle type information of each trip are extracted; based on the toll record set, the entry toll station, exit toll station, entry time and path of each trip are extracted.

[0075] The scenario of this application is an offline statistical analysis scenario, which is used to analyze the changes in toll collection amounts caused by the traffic event to various highway management entities after the life cycle of the traffic event ends.

[0076] During data preprocessing, let Ω be a set of given highway toll records fee_item, divide the records of Ω according to the travel_id trip identifier field, and for any trip identifier tid, obtain the toll record set fee_items(tid) of different tid-identified trips and the vehicle type car_typ.

[0077] Furthermore, the entrance toll station O(tid), the exit toll station D(tid), and the time BgnTime(tid) when the vehicle enters the entrance toll station are extracted from fee_items(tid). The path of fee_items(tid) is restored on the highway network to obtain the path restoration result path(tid).

[0078] Store the results of all travel data and get the result U tid O(tid), the set of all entrance toll booths; U tid D(tid), the set of all exit toll booths; U tid path(tid), the set of all path restoration results; U tid (O(tid), D(tid), path(tid)), the entrance toll station, exit toll station and path of each trip, that is, the OD path set in this application, the OD path set refers to U tid(O(tid), D(tid), path(tid)); and U tid (O(tid), D(tid), path(tid), car_type, BgnTime(tid)). The entry toll station, exit toll station, path, car type and entry time of each trip, that is, the driving information set of the trip.

[0079] Furthermore, based on the OD path set and the road section where the traffic event occurs, a plurality of OD pairs affected by the traffic event are determined.

[0080] Based on the obtained OD path set: U tid (O(tid), D(tid), path(tid)) As a result, we get the relationship between any O, D and the path that can pass between OD. As we know above, a path is composed of several topologically connected road segments. When we know the traffic event When the affected road sections are found, it is possible to find out which ODs will be affected through the road sections.

[0081] Specifically, the target road section where the traffic event occurs is obtained, and the road sections included in each path of each pair of ODs are analyzed. When any path between ODs includes the target road section, it is determined that OD is affected.

[0082] In the method of the embodiment of the present application, after determining the target road section where the traffic incident occurred, as long as any path between OD includes the target road section, the OD pair will be affected. Because when a traffic incident occurs on a path between OD, vehicles may choose to travel other paths, so the traffic volume on each path may change, and therefore the entrance toll station and the exit toll station will be affected.

[0083] In one implementation, the method further includes: obtaining a path including the target road segment as the target path; determining whether the target path includes a sub-path; and if the target path includes the sub-path, using the OD corresponding to the sub-path as the affected OD.

[0084] Specifically, determining whether the target path contains a subpath includes: obtaining the intersection of a certain path and the target path; calculating the ratio of the intersection to a certain path; and determining that a certain path is a subpath of the target path when the ratio is greater than or equal to a preset threshold. The specific value of the preset threshold is not specifically limited in this application.

[0085] When a traffic incident occurs, a long trip may be split into several short trips, which means that the number of long trips decreases and the number of short trips may increase. These are the effects of traffic incidents and should be taken into account when analyzing toll changes. Therefore, it is necessary to determine whether the short trip is an approximate sub-path of the long trip (the strict sub-path is not used because it may be necessary to use other roads for detours.)

[0086] Let U tid path(tid) is the vehicle travel path set formed based on the highway toll records in the first step. tid path(tid) is merged, that is, the itineraries with the same path are merged to obtain the vehicle travel path set U after deduplication. tid ′path(tid).

[0087] Set path i and path j U tid Any two paths in ′path(tid), determine path i Is it a path? j The subpath method is as follows:

[0088]

[0089] The meaning of this formula is to obtain the intersection of a certain path and the target path; calculate the ratio of the intersection to a certain path; and determine that a certain path is a subpath of the target path when the ratio is greater than or equal to a preset threshold. Among them, tld is the set similarity threshold.

[0090] If path i is path j The subpath of path can be obtained i The set of road segments included in the path to be avoided is:

[0091] otr(path i , path j )={link|link∈(path j -path i )}

[0092] If in otr(path i , path j ) occurs, then path i An increase in the number of trips can be associated with traffic events.

[0093] like Figure 7 As shown, there are two paths between O1 and D1, pathl = link a，b→link b，c →link c，d →link d,e →link e，f →link f，h and path2=link a,b →link b,c →link c，g →link g，f →link f，h . Assume link e，f Traffic incident So we can infer the traffic incident Affects the passage between O1→D1. Because link e，f It also belongs to the section between O2→D1. Similarly, the traffic event Affects the passage between O2→D1.

[0094] Furthermore, the accessible path between O1→D2 is path3=link a，b →link b,c →link c，d →link d，e is a subpath of the accessible path pathl between O1→D1, and the traffic event Occurs on otr(path3, path1), so O1→D2 is also included in the traffic event Scope of influence.

[0095] According to this step, it is possible to obtain which OD pairs are affected by the traffic event.

[0096] S102 determines the time range of the traffic event's impact based on the occurrence time and end time of the traffic event.

[0097] In one embodiment, based on the occurrence and end time of the traffic event, the time range of the traffic event impact is determined, including: taking the occurrence time of the traffic event as the start time of the traffic event impact; tracing back the end time of the traffic event to the time obtained after the first time as the end time of the traffic event impact, wherein the first time refers to the expected time for upstream vehicles to travel to the diversion point. It can be understood that in one embodiment of the present application, tracing back refers to taking the end time of the traffic time as the starting point, tracing back to the first time period, that is, the end time of the traffic event impact.

[0098] Generally, after a major traffic incident occurs, congestion of a certain scale can be reached very quickly. Due to the high penetration rate of high-speed travel navigation services, users can quickly learn about the occurrence of traffic incidents, which in turn affects the travel intentions of different vehicles and the choice of travel routes. Therefore, the start time of the traffic incident impact can be recorded as the time period when the incident occurs. Figure 7 Zhongwei The moment.

[0099] After the impact of the traffic incident disappears, in theory, the vehicle's travel behavior will return to the state before the traffic incident. Also, due to the high penetration rate of navigation services, travel users can know the end of the traffic incident in time and return to their behavioral preferences before the traffic incident, but the timing of this decision may vary for different ODs.

[0100] Still Figure 7 For example, point c is the decision point for the splitting of paths path1 and path2. Traffic incidents at all times The influence of ends, then the vehicles near point c face the choice between path1 and path2. Theoretically After time c, the vehicle behavior before point c should be restored to the traffic event However, since O1 and O2 take different time to reach point c, the traffic event The impact on O1→D1 ends at where t a，c is the expected time for a vehicle to travel from point a to point c; similarly, traffic events The impact on O2→D1 ends at Traffic incident The end time of the impact on the O2→D1 path is the time obtained by the difference between the end time of the traffic event and the first time, that is, the end time of the impact of the traffic event.

[0101] It can be understood that in some embodiments, the impact of each 0D in the 0D set affected by the traffic event on the vehicles on the corresponding road section is different, which is related to whether the vehicle is before the divergence point and the expected value of the vehicle's travel time from the divergence point.

[0102] S103 counts the traffic volume corresponding to the target vehicle type on each path between each pair of ODs based on the time range, and obtains the change of the traffic volume on the path between ODs by comparing the difference with the benchmark value.

[0103] Before counting the traffic volume corresponding to the target vehicle type on each path between each pair of ODs, the difference compared to the baseline value also includes:

[0104] Characteristic days are divided based on date features, and a 24-dimensional data sequence corresponding to each characteristic day is obtained, which is a mapping of the travel information set of multiple trips divided by hours. The data sequence is cleaned to eliminate data from time periods related to traffic incidents. Based on the cleaned data, the traffic volume baseline value from point O to point D based on a certain path for the target vehicle model in the corresponding time period of each characteristic day is obtained.

[0105] Specifically, Figure 6 As shown in the figure, there are one or more possible driving paths between each OD. Different car types have preferences for path selection, so the car_type variable needs to be referred to when analyzing path selection. In addition, vehicles in different time periods also have different choices for paths, so the time_span data also needs to be referred to.

[0106] In summary, the data obtained in the first step is combined with (O, D, path, car_type) as the joint index, and the two attributes of Day and Time are extracted based on the BgnTime field. The characteristic time period described in the data is divided according to Time, and the number of times that the (O, D, path, car_type) data set falls in a certain characteristic time period on Day is counted. Since there are 24 characteristic time periods every day, the daily (O, D, path, car_type) data set can be mapped to a 24-dimensional data sequence, and each dimension of data is the number of trips that enter O during the characteristic time period on Day, arrive at D via path, and the vehicle type is car_type.

[0107] Day is divided according to characteristic days, such as distinguishing between working days, weekends, holidays, etc. For each characteristic day, a 24-dimensional data sequence mapped by several (O, D, path, car_type) data sets can be obtained. The above data sequence set is cleaned (data related to the period when major traffic incidents occur are removed to obtain basic data without traffic incidents). The car model is car_type. This characteristic day meets the benchmark number of trips from O to D via path in each characteristic period, which is recorded as:

[0108]

[0109] in, is the expected number of benchmark trips from k-1 to k on the feature day when the vehicle type is car_type and the path path is from O to D.

[0110] Furthermore, based on the determined impact time range, under the impact time range constraint, by statistically analyzing the difference between dst_cnt(O, D, path, car_type) and the benchmark value std_cnt(O, D, path, car_type), the change in the traffic volume between OD paths affected by the traffic event in each time period can be obtained.

[0111] According to this step, within the determined impact time range, the traffic volume data of the target vehicle type on each path between each pair of ODs is collected. The traffic volume changes on each path between ODs before and after the traffic incident are compared to analyze the impact of the traffic incident on vehicle path selection.

[0112] S104 analyzes the change of toll between OD based on the change of traffic volume between OD paths.

[0113] In one embodiment, the toll of each vehicle can be calculated based on the vehicle type and the driving path. For each OD pair path, the tolls of all vehicles are accumulated to obtain the total toll of the path.

[0114] Compare the changes in total tolls for each OD pair before and after the traffic incident. Analyze the impact of traffic volume changes on tolls and determine whether the tolls increased or decreased due to the traffic incident.

[0115] In an exemplary scenario, between a given OD and a path that can pass between ODs, if multiple traffic events occur in a certain period of time, it also includes: determining the weight of each traffic event based on the congestion length corresponding to each traffic event; taking a weighted sum of the weight of each traffic event and the change in traffic volume on the path between ODs affected by each traffic event, and determining the change in traffic volume on the path between ODs under the influence of multiple traffic events.

[0116] Similarly, for a given O, D and a path that can pass between OD, suppose another traffic event in a certain period of time Also related to it, then according to this period and The weights are calculated based on the congestion degree, and the changes in traffic volume between OD paths are divided according to the weights.

[0117] For a given starting point O, end point D, and a path between OD, if there is another related traffic event φ′ in a certain period of time, then we need to calculate the weight according to the congestion level of φ and φ′ in this period of time, and divide the change of traffic volume on the path between OD accordingly.

[0118] For each traffic event φ and φ′, the congestion length caused by it in a specific period is counted. The congestion length calculation method in the prior art can be used to calculate the congestion length, and a weight can be assigned to each traffic event according to the congestion length. For example, if the congestion length of φ is twice that of φ′, then the weight of φ may be twice that of φ′.

[0119] Divide the traffic volume changes between OD paths according to the weights. If the weight of φ is twice that of φ', then the contribution of φ to the traffic volume change may be twice that of φ'.

[0120] Based on the changes in traffic volume after the division, the impact of each traffic event on tolls is analyzed. This involves calculating the changes in vehicle paths caused by each event and the contribution of these changes to tolls. The impact of all relevant traffic events on tolls is comprehensively evaluated to obtain the final toll change.

[0121] The embodiment of the present application determines the multiple OD pairs affected and the affected time range based on the road section where the traffic incident occurs, and counts the changes in the traffic volume between the OD paths corresponding to the target vehicle type on each path between each pair of ODs within the corresponding time range, and then analyzes the changes in tolls between ODs.

[0122] First, this method uses statistics based on group behavior as a measurement. Compared with the model based on the vehicle perspective, which requires estimation of the individual behavior of a single vehicle, it can significantly reduce the random error of the estimation and improve the accuracy of the analysis.

[0123] Secondly, it breaks the assumption in traditional models that vehicles strictly follow the shortest path. By comparing and analyzing the changes in traffic volume between OD paths on normal days and abnormal days, it can fully capture the diversity of vehicle behaviors when traffic incidents occur. This method can not only cover the various action paths that vehicles may take when facing traffic incidents, including detours, early or delayed entry to the highway, etc., but also quantify the specific impact of these behaviors on highway toll revenue.

[0124] Finally, this method can also quantify the contribution of tolls between different ODs, clarify the focus of operation guarantee, optimize resource allocation, and give priority to solving traffic incidents that lead to toll loss, thereby improving the operational efficiency and economic benefits of highways and providing a data basis for relevant departments to optimize highway operation and operation business.

[0125] According to another aspect of the embodiment of the present application, a device for analyzing the impact of traffic events on tolls is provided for implementing the above-mentioned method for analyzing the impact of traffic events on tolls. Figure 8 As shown, the device comprises:

[0126] The first analysis module 801 is used to determine a plurality of OD pairs affected by a traffic event based on an OD path set and a road section where the traffic event occurs;

[0127] The second analysis module 802 is used to determine the time range of the traffic event based on the occurrence time and end time of the traffic event;

[0128] The third analysis module 803 is used to count the traffic volume corresponding to the target vehicle type on each path between each pair of ODs based on the time range, and obtain the change of the traffic volume between the OD paths compared with the difference of the benchmark value;

[0129] The statistical module 804 is used to analyze the change of toll between OD based on the change of traffic volume between OD paths.

[0130] It should be noted that the device for analyzing the impact of traffic events on tolls provided in the above embodiment only uses the division of the above functional modules as an example when executing the method for analyzing the impact of traffic events on tolls. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device for analyzing the impact of traffic events on tolls provided in the above embodiment and the method for analyzing the impact of traffic events on tolls are of the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0131] According to another aspect of an embodiment of the present application, an electronic device corresponding to the method for analyzing the impact of traffic events on tolls provided in the aforementioned embodiment is also provided to execute the aforementioned method for analyzing the impact of traffic events on tolls.

[0132] Please refer to Fig. 9 , which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Fig. 9 As shown, the electronic device includes: a processor 900, a memory 901, a bus 902 and a communication interface 903, and the processor 900, the communication interface 903 and the memory 901 are connected via the bus 902; the memory 901 stores a computer program that can be run on the processor 900, and when the processor 900 runs the computer program, it executes the method for analyzing the impact of traffic events on tolls provided in any of the aforementioned embodiments of the present application.

[0133] The memory 901 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 903 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0134] The bus 902 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 901 is used to store programs, and the processor 900 executes the program after receiving the execution instruction. The method for analyzing the impact of traffic events on tolls disclosed in any implementation of the above-mentioned embodiment of the present application can be applied to the processor 900, or implemented by the processor 900.

[0135] The processor 900 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 900. The above processor 900 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 901, and the processor 900 reads the information in the memory 901 and completes the steps of the above method in combination with its hardware.

[0136] The electronic device provided in the embodiment of the present application and the method for analyzing the impact of traffic events on tolls provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0137] According to another aspect of the embodiments of the present application, a computer-readable storage medium corresponding to the method for analyzing the impact of traffic events on tolls provided in the aforementioned embodiments is also provided, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the method for analyzing the impact of traffic events on tolls provided in any of the aforementioned embodiments.

[0138] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0139] The computer-readable storage medium provided in the above-mentioned embodiment of the present application and the method for analyzing the impact of traffic events on tolls provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0140] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A method for analyzing the impact of traffic events on tolls, characterized in that: include: Based on the OD path set and the road section where the traffic event occurs, determining a plurality of OD pairs affected by the traffic event; Determine the time range of the traffic event's impact based on the traffic event's occurrence and end times; Based on the time range, the traffic volume corresponding to the target vehicle type on each path between each pair of ODs is counted, and the difference compared with the benchmark value is used to obtain the change of the traffic volume between the OD paths; Based on the change in traffic volume between the OD paths, the change in toll between the OD paths is analyzed.

2. The method according to claim 1, characterized in that Before determining a plurality of OD pairs affected by the traffic event based on the OD path set and the road section where the traffic event occurs, the method further includes: The toll records within the preset analysis period are divided into data according to the trip identifier, and the toll record set and vehicle type information of each trip are extracted; based on the toll record set, the entry toll station, the exit toll station, the entry time and the path of each trip are extracted; Based on the entrance toll station, exit toll station and path of each trip, the OD path set of all trips is obtained; Based on the entrance toll station, exit toll station, route, vehicle type and entry time of each trip, a set of driving information of all trips is obtained.

3. The method according to claim 1, characterized in that Based on the OD path set and the road section where the traffic event occurs, a plurality of OD pairs affected by the traffic event are determined, including: Acquire the target road section where the traffic incident occurs; The road segments included in each path of each pair of ODs are analyzed, and when any path between the ODs includes the target road segment, it is determined that the OD is affected.

4. The method according to claim 3, characterized in that Also includes: Obtaining a path including the target road segment as the target path; Determine whether the target path contains a sub-path; In the case that the target path includes the sub-path, the OD corresponding to the sub-path is used as the affected OD.

5. The method according to claim 4, characterized in that Determining whether the target path contains a sub-path includes: Get the intersection of a certain path and the target path; Calculating a ratio of the intersection to a certain path; When the ratio is greater than or equal to a preset threshold, a certain path is determined to be a sub-path of the target path.

6. The method according to claim 1, characterized in that Based on the occurrence time and end time of the traffic event, determining the time range of the traffic event's impact includes: The time when the traffic event occurs is used as the start time of the impact of the traffic event; The end time of the traffic event is traced back to the time after the first time as the end time of the traffic event, where the first time refers to the expected time for upstream vehicles to travel to the diversion point.

7. The method according to claim 1, characterized in that Based on the time range, the traffic volume corresponding to the target vehicle type on each path between each pair of ODs is counted, and before the difference compared with the benchmark value, it also includes: Divide the characteristic days based on date features, and obtain a 24-dimensional data sequence mapped by a set of travel information of multiple trips corresponding to each characteristic day; Performing data cleaning on the data sequence to remove data from the time period related to the occurrence of traffic incidents; Based on the cleaned data, the traffic volume baseline value of the target vehicle model from point O to point D based on a certain path in the corresponding period of each characteristic day is obtained.

8. The method according to claim 1, characterized in that: If multiple traffic events occur in a certain period of time between a given OD and a path that can pass between ODs, it also includes: Determine the weight of each traffic event based on the congestion length corresponding to each traffic event; The weight of each traffic event and the change in traffic volume between OD paths affected by each traffic event are weighted and summed to determine the change in traffic volume between OD paths under the influence of multiple traffic events.

9. A device for analyzing the impact of traffic events on tolls, characterized in that: include: A first analysis module is used to determine a plurality of OD pairs affected by the traffic event based on the OD path set and the road section where the traffic event occurs; A second analysis module is used to determine the time range of the traffic event based on the occurrence time and end time of the traffic event; The third analysis module is used to count the traffic volume corresponding to the target vehicle type on each path between each pair of ODs based on the time range, and obtain the change of the traffic volume between the OD paths compared with the difference of the benchmark value; The statistical module is used to analyze the change of toll between OD based on the change of traffic volume between OD paths.

10. An electronic device, characterized in that: The system comprises a processor and a memory storing program instructions, wherein the processor is configured to execute the method for analyzing the impact of traffic events on tolls as claimed in any one of claims 1 to 8 when executing the program instructions.

11. A computer readable medium, characterized in that Computer-readable instructions are stored thereon, and the computer-readable instructions are executed by a processor to implement a method for analyzing the impact of a traffic event on tolls as described in any one of claims 1 to 8.

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