Urban agglomeration comprehensive transportation channel identification method and system based on mobile phone signaling data

Abstract

The invention provides an urban agglomeration comprehensive transportation channel identification method and system based on mobile phone signaling data, and belongs to the technical field of traffic planning and intelligent traffic, and the method comprises the steps: carrying out the cleaning and speed calculation of the mobile phone signaling data, removing an abnormal track, and forming a reliable inter-city travel OD sample; each OD is accurately divided into a railway or road travel mode by taking a station POI as an anchor point and combining a moving speed threshold value; aiming at different travel modes, passenger flow is distributed on a real road network by applying a two-way A * gradually-shortened path algorithm, high-flow sections are combined, and quantitative identification of a comprehensive transportation channel is realized. According to the method, through deep fusion of the mobile phone signaling data and the road network structure, the recognition precision and reliability are remarkably improved, and a scientific basis is provided for planning, construction and management of the comprehensive transportation channel of the urban agglomeration.

Description

Technical Field

[0001] This invention relates to the field of transportation planning and intelligent transportation technology, specifically to a method and system for identifying integrated transportation corridors in urban agglomerations based on mobile phone signaling data. Background Technology

[0002] With the accelerating pace of globalization and the continuous improvement of urbanization, urban agglomerations, as an emerging urban development model, have gradually become an important force driving economic growth and social development. The formation of urban agglomerations not only promotes coordinated regional economic development but also places higher demands on transportation systems. The increasing travel demand between important urban nodes and the significant increase in route traffic necessitate efficient transportation through multiple modes of transport, and the collection of these transport routes can be defined as integrated transportation corridors. Scientific management and rational utilization of integrated transportation corridors can optimize the allocation of road network traffic flow, effectively shorten travelers' travel time, and thus significantly improve overall transportation efficiency. However, most current studies tend to conduct analyses based on the assumption that integrated transportation corridors are already known, neglecting in-depth exploration of the identification process. This research approach may lead to the omission of some integrated transportation corridors with indistinct characteristics and fail to fully reflect the changes in the characteristics of integrated transportation corridors under different conditions. Therefore, accurate identification of integrated transportation corridors in real road network environments and in-depth analysis of their characteristic changes have become key requirements for further optimizing the utilization efficiency of integrated transportation corridors.

[0003] Currently, several methods exist for identifying integrated transportation corridors, primarily focusing on qualitative and quantitative identification. Qualitative identification methods emphasize analyzing the functions and attributes of urban nodes and road segments. This involves assessing indicators such as urban economic scale, population density, and transportation infrastructure, or employing social network analysis and multi-indicator comprehensive evaluation methods to determine whether a transportation corridor has formed between cities. This method is highly subjective and requires adjustments based on specific scenarios and expert experience. Quantitative identification methods, on the other hand, analyze traffic flow data, such as the number of vehicles passing through road segments, transportation time, and cargo volume, to identify high-flow road segments and designate them as integrated transportation corridors. Common analysis methods include traffic flow analysis, shortest path analysis, and maximum flow analysis. This method relies on data-driven approaches and can accurately capture frequently used transportation routes, but it has some limitations. First, a comprehensive, all-weather sensor network for road segment-level traffic monitoring within urban agglomerations has not yet been established, and the technology for directly identifying integrated transportation corridors using road segment traffic flow is not yet mature. Secondly, to reduce computational complexity, most studies first perform "topological simplification" on real road networks, such as merging parallel roads, eliminating low-level roads, or even simplifying several consecutive road segments into a single "hyperedge." While this abstraction improves algorithm efficiency, it severs the "actual operability" of the pathways. Thirdly, traditional shortest path or maximum flow algorithms generally assume that travelers have complete information and pursue a single objective (minimum time or cost), ignoring the widespread random factors in reality such as "perceptual errors, risk preferences, travel habits, and incomplete information." Finally, existing quantitative models often simply convert the flow of various transportation modes, such as highways, railways, waterways, aviation, and even pipelines, into equivalent carloads or "ton-kilometers" and directly perform shortest path, maximum flow, or gravity model calculations on the same network layer, without quantitatively identifying each mode of transport. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for identifying integrated transportation corridors in urban agglomerations based on mobile phone signaling data, so as to solve at least one of the technical problems existing in the background art.

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

[0006] In a first aspect, the present invention provides a method for identifying integrated transportation corridors in urban agglomerations based on mobile phone signaling data, comprising: cleaning and speed calculation of mobile phone signaling data, removing abnormal trajectories, and forming reliable intercity travel origin-destination (OD) samples; using station point of interest (POI) as anchor points and combining movement speed thresholds, accurately classifying each OD into railway or highway travel modes; and for different travel modes, using a bidirectional A* gradually shortening path algorithm on a real road network to allocate passenger flow, merging high-flow sections, and achieving quantitative identification of integrated transportation corridors.

[0007] As a further limitation of the first aspect of the present invention, the mobile phone signaling data is cleaned by removing drift data and abnormal trajectories of the ping-pong effect through timestamp conversion and speed calculation, and the intercity travel OD samples are retained; the road network data and railway station POI data of the study area are imported and spatial alignment and visualization verification are performed.

[0008] As a further limitation of the first aspect of the present invention, spatial matching is performed between the latitude and longitude of mobile phone signaling data and the POI of railway stations; for special travel chains that arrive at railway stations after crossing administrative divisions, the entire journey is directly and forcibly classified as railway to eliminate boundary misjudgment; the OD around the station is further judged by speed threshold; thus, each OD is given a precise label of highway, ordinary railway or high-speed railway, and the proportion distribution of the three travel modes is finally output.

[0009] As a further limitation of the first aspect of the present invention, the OD traffic of the completed travel mode marking is imported into the real road network, and passenger flow is allocated according to the differences in traveler behavior. After the allocation is completed, the railway and highway lines are divided into several sections based on the administrative boundaries of prefecture-level cities, and the average transport volume of each section is calculated to screen out high-flow sections. The passenger flow of the two modes of transport is weighted by mileage and summed. Sections containing only a single mode of transport are eliminated, and the remaining high-flow sections are merged to form a comprehensive transport corridor, thereby obtaining a quantitative identification result that can accurately reflect the actual transport demand of the urban agglomeration.

[0010] As a further limitation of the first aspect of the present invention, the passenger flow allocation based on differences in traveler behavior includes: railway passenger flow directly uses the bidirectional A* algorithm to search for the shortest path, while highway passenger flow uses the K progressively shorter path technique. First, the shortest path is obtained using the bidirectional A* algorithm, and then the second shortest and third shortest paths are generated sequentially through edge deletion and edge pair deletion strategies, thereby ensuring computational efficiency while covering the random route selection behavior of highway travelers; subsequently, the highway route selection probability is calculated based on the travel time cost and grade mileage of each path, and the OD traffic is allocated to the corresponding road segments proportionally.

[0011] As a further limitation of the first aspect of the present invention, the search process of the bidirectional A-star algorithm is as follows:

[0012] Step 1: Create two open lists. Open list 1 searches for paths from the origin to the destination, and open list 2 searches for paths from the destination to the origin. Initialize the total estimated cost, actual cost, and projected cost for the origin and destination. Add the origin to open list 1 and the destination to open list 2.

[0013] Step 2: Extract the node with the smallest total estimated cost from the two open lists and mark it as node O and node D respectively; when the two nodes overlap to become the same node, the path search is considered complete; otherwise, continue to search the neighboring nodes of the two nodes; calculate the total estimated cost of all neighboring nodes of node O and the total estimated cost of all neighboring nodes of node D, and add them to open list 1 and open list 2 respectively.

[0014] Step 3: After the path search is completed, merge the two search paths to reverse the process and construct the complete path, then output the shortest path information;

[0015] Step 4: First, delete one edge from the shortest path in the traffic network, and repeat steps 1, 2, and 3 to calculate the initial secondary shortest path;

[0016] Step 5: Repeat step 4 until all edges in the shortest path have been deleted. Compare all the temporary shortest paths and select the shortest one as the second shortest path.

[0017] Step 6: When K≥3, pair all edges in the first (K-1) shortest paths, delete one edge pair at a time to calculate the temporary shortest path, until all edge pairs have been deleted, compare all the obtained temporary shortest paths, and the shortest path is the Kth shortest path.

[0018] Secondly, the present invention provides a city cluster integrated transportation corridor identification system based on mobile phone signaling data, comprising:

[0019] The preprocessing module is used to clean and calculate the speed of mobile phone signaling data, remove abnormal trajectories, and form reliable intercity travel OD samples;

[0020] The segmentation module is used to accurately classify each OD (Original Destination) into rail or road travel modes, using the site POI as the anchor point and combining it with the movement speed threshold.

[0021] The identification module is used to allocate passenger flow on a real road network using the bidirectional A* gradually shortening path algorithm for different modes of travel, merge high-volume sections, and achieve quantitative identification of integrated transportation corridors.

[0022] Thirdly, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the urban agglomeration integrated transportation corridor identification method based on mobile phone signaling data as described in the first aspect.

[0023] Fourthly, the present invention provides a computer device including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the urban agglomeration integrated transportation channel identification method based on mobile phone signaling data as described in the first aspect.

[0024] Fifthly, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the urban agglomeration integrated transportation corridor identification method based on mobile phone signaling data as described in the first aspect.

[0025] The beneficial effects of this invention are: by deeply integrating mobile phone signaling data with road network structure, the recognition accuracy and reliability are significantly improved, providing a scientific basis for the planning, construction and management of integrated transportation corridors in urban agglomerations.

[0026] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating the mode of travel classification as described in an embodiment of the present invention. Detailed Implementation

[0029] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0030] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as here.

[0032] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0033] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0034] To facilitate understanding of the present invention, the present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0035] Those skilled in the art should understand that the accompanying drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0036] This invention proposes a method for identifying integrated transportation corridors in urban agglomerations based on mobile phone signaling data. By properly processing mobile phone information data to filter origin-destination (OD) points and rationally classify travel modes, the method improves the quantitative identification effect of integrated transportation corridors within urban agglomeration areas. This invention extracts OD data for the Beijing-Tianjin-Hebei region using mobile phone signaling data and classifies travel modes based on average transport speed, point of interest (POI) information, and road network structure. Subsequently, considering the randomness of travelers' route choices, this invention uses a gradually shortening path algorithm to distribute passenger flow across the real road network for different modes of transportation and statistically analyzes the passenger flow volume of each route. This data is then used as a judgment parameter to achieve quantitative identification of integrated transportation corridors.

[0037] Example 1

[0038] In this embodiment 1, a comprehensive urban agglomeration transportation corridor identification system based on mobile phone signaling data is first provided. This system includes: a preprocessing module for cleaning and speed calculation of mobile phone signaling data, removing abnormal trajectories, and forming reliable intercity travel origin-destination (OD) samples; a segmentation module for accurately classifying each OD into rail or road travel modes using station point-of-purchase (POI) points and travel speed thresholds; and an identification module for allocating passenger flow on a real road network using a bidirectional A* progressively shorter path algorithm for different travel modes, merging high-volume sections, and achieving quantitative identification of comprehensive transportation corridors.

[0039] In this embodiment, based on the above system, a method for identifying urban agglomeration integrated transportation corridors based on mobile phone signaling data is implemented, including the following steps:

[0040] This method includes mobile signaling data preprocessing, traveler mode classification, and comprehensive transportation channel identification based on passenger flow allocation algorithm.

[0041] (1) Mobile signaling data preprocessing

[0042] In this step, we clean the mobile signaling data, removing drift data and ping-pong effect anomalies through timestamp conversion and velocity calculation, retaining intercity travel origin-destination (OD) samples. We import road network data (including railways, expressways, and primary / secondary highways) and railway station POI data for the study area, and perform spatial alignment and visualization verification. This process provides a data foundation for subsequent traveler mode segmentation and integrated transportation corridor identification based on passenger flow allocation algorithms.

[0043] (2) Method of classifying travel modes

[0044] In this step, we categorize travel behaviors. First, we spatially match the latitude and longitude coordinates from mobile signaling data with the POIs of railway stations: using the station coordinates as the center, we calculate the straight-line distance between the trajectory point and the station using the Haversine formula. If this distance is ≤5km, the OD is marked as "near the station"; otherwise, it is initially classified as road travel. Based on this, for special travel chains that cross administrative boundaries to reach the railway station, we directly and forcibly classify the entire journey as railway to eliminate boundary misjudgments. Subsequently, we perform a second discrimination on the "near the station" ODs using a speed threshold: we compare the actual travel speed with the average operating speed of the railway line (obtained in real-time by dividing the line length by the historical average travel time). If the speed is higher than the high-speed rail average, it is marked as high-speed rail travel; if it is between high-speed rail and conventional rail, it is marked as conventional rail travel; and if it is lower than the conventional rail average, it reverts to road travel. At this point, each OD is assigned a precise label of road, conventional rail, or high-speed rail, and the final output shows the proportional distribution of the three travel modes.

[0045] (3) Identification of integrated transportation corridors

[0046] In this step, we complete the identification of integrated transportation corridors. First, the OD traffic with marked travel modes is imported into the real road network, and passenger flow is allocated according to differences in traveler behavior: railway passenger flow directly uses the bidirectional A* algorithm to search for the shortest path, while highway passenger flow uses the K progressively shorter path (K=3) technique—that is, first the shortest path is found using bidirectional A*, and then the second shortest and third shortest paths are generated sequentially through edge deletion and edge pair deletion strategies—thereby ensuring computational efficiency while covering the random route selection behavior of highway travelers; then, the highway route selection probability is calculated by weighting the travel time cost and grade mileage of each path, and the OD traffic is allocated to the corresponding road segments proportionally. After allocation, the railway and highway lines are divided into several sections based on the administrative boundaries of prefecture-level cities. The average transport volume of each section is calculated, and high-volume sections are selected using 5,000 passenger trips (railway) and 20,000 passenger trips (highway) as thresholds. Finally, the passenger volume of the two modes of transport is weighted by mileage and summed. Sections containing only a single mode of transport are eliminated, and the remaining high-volume sections are merged to form a comprehensive transport corridor, thus obtaining a quantitative identification result that can accurately reflect the actual transport demand of the urban agglomeration.

[0047] Example 2

[0048] This embodiment proposes a quantitative identification method for integrated transportation corridors based on mobile phone signaling data. Through rigorous data preprocessing to remove invalid data, and combining speed, location information, and Points of Interest (PoI) information from mobile phone signaling data, accurate segmentation of different travel modes is achieved. Using a progressively shorter path algorithm, passenger flow for different transportation modes is precisely allocated on the real road network, and detailed statistics on passenger transport volume for each route are performed. Finally, through comprehensive analysis of the transport volume results, accurate identification results of integrated transportation corridors are obtained, providing a scientific basis for the optimized management of integrated transportation corridors in urban agglomerations.

[0049] The quantitative identification method for integrated transportation channels based on mobile phone signaling data described in this embodiment includes the following steps:

[0050] 1. Clean and speed-calculate mobile phone signaling data, remove abnormal trajectories, and form reliable intercity travel origin-destination (OD) samples.

[0051] 2. Using the POI of a station as an anchor point and combining it with the movement speed threshold, each OD is accurately classified into rail or road travel mode.

[0052] 3. For different modes of travel, the bidirectional A* gradual shortening path algorithm is used on the real road network to allocate passenger flow, merge high-volume sections, and realize the quantitative identification of integrated transportation channels.

[0053] Step 1 is the data preprocessing process. First, the time format is converted, and then the vehicle speed is calculated by combining the traveler's latitude and longitude information with their travel time. Addressing issues caused by anomalies such as the ping-pong effect, this method encompasses three core stages: raw data conversion, anomaly data processing, and road network data import.

[0054] (1) Raw Data Conversion. In the data unit conversion stage, the initial millisecond-level Unix timestamps are converted into a standard date-time format. The time difference between the origin and destination is calculated in seconds. Then, this time difference is divided by the trajectory distance to obtain the instantaneous speed in m / s. This is then multiplied by 3.6 to convert it to km / h, thus completing the speed information calculation process.

[0055] (2) Outlier Handling. Outlier data mainly comes from two sources: drift data and the ping-pong effect. The specific handling methods are as follows:

[0056] ① Drift Data: Drift data refers to records where the physical distance (OD distance) between the departure grid and the arrival grid in a single user's movement trajectory exceeds a reasonable threshold (default is 1000 kilometers, which can be adjusted according to specific scenarios). First, determine if the travel data falls within the studied area. Based on the county / district codes of the departure and arrival locations, filter travelers within the studied area and retain travel data that meets the requirements. Then, determine if it is intercity travel data by checking whether the travel distance is between 1 km and 1500 km and the travel speed is between 5 km / h and 350 km / h. If the data does not meet either of these conditions, it is classified as drift data and deleted.

[0057] ② Ping-Pong Effect: The ping-pong effect refers to the situation where, for the same user with the same starting grid ID, the time interval between the previous departure from the grid and the subsequent entry into the grid is less than a set threshold in two consecutive records. Trajectory data needs to be grouped based on the user's unique identifier (e.g., user IMSI) to ensure that a single user's trajectory can be analyzed independently. Records within each group are sorted in ascending order by "starting grid entry time" to ensure the continuity of trajectory time. The sorted user records are iterated through, and adjacent records are judged as follows: Condition 1, the two records have the same "starting grid ID"; Condition 2, the time difference (in seconds) between the "starting grid entry time" of the later record and the "starting grid departure time" of the previous record is less than a threshold (e.g., 300 seconds). If both conditions are met, the record is determined to be ping-pong effect data and is deleted.

[0058] (3) Road Network Data Import: The comprehensive transportation corridors within the study area mainly consist of urban nodes, railway lines, and highways. Urban areas are divided based on county-level administrative regions. Railway lines include both ordinary passenger railway lines and high-speed railway lines, while highways include expressways, first-class highways, and second-class highways. The acquisition and visualization of regional road network data mainly involves three key steps: acquiring road network data, acquiring station data, and data visualization.

[0059] ① Obtain road network data: Use the database in OpenStreetMap to query the required road network data. After selecting specific types of route data, export the data for subsequent visualization processing.

[0060] ② Obtaining Station Data: The system calls the Gaode API interface and utilizes the railway station query API to retrieve railway station information within the search range. This information includes station names, latitude and longitude coordinates, etc. For any missing station names, the geocoding API is used to convert them into latitude and longitude coordinates to obtain the station's geographical location. The data is then exported and saved using a Python JSON model.

[0061] ③ Data Visualization: Launch the GIS software, add the corresponding layers, and import the OSM road network data and the station data obtained through the Gaode API, ensuring that the station locations are correctly aligned with the road network. Use spatial analysis tools to check whether the stations are located within specific road segments. Use different colors and symbols to visualize the road network data and station data, and compare and verify them with Gaode Map to ensure the authenticity and completeness of the road network data.

[0062] like Figure 1 As shown, step 2 is the process of travel mode classification. This method selects to use information such as latitude, longitude and speed in the data and PoI information to classify the travel mode of travelers and count the proportion of different travel modes. This method covers two core links: latitude and longitude matching and speed threshold judgment.

[0063] ① Latitude and Longitude Matching: In the initial stage of travel mode identification, this invention compares the PoI information of railway stations with the real-time location coordinates of travelers to calculate the straight-line distance between them. When this distance is lower than a preset threshold (e.g., 5 kilometers), it can be determined that the traveler is in the vicinity of the railway station. For travelers located outside the vicinity of the railway station, it is preliminarily inferred that their travel mode is road transportation. In addition, this method is specifically optimized for a special case: when the same traveler crosses different administrative divisions to reach the vicinity of a railway station and chooses railway transportation, the entire journey of the traveler is uniformly classified as railway travel. This processing mechanism can effectively avoid misjudgment of travel mode due to differences in administrative divisions, ensuring the accuracy and completeness of travel mode classification. The straight-line distance calculation is expressed as:

[0064]

[0065] In the formula, a is an intermediate calculated value; Δlat = lat1 - lat2 is the difference between two latitudes; Δlon = lon1 - lon2 is the difference between two longitudes; R is the average radius of the Earth, approximately 6371 kilometers; and d is the distance between the two points.

[0066] ② Speed ​​Threshold Judgment: Based on location information matching, this method further classifies travelers near railway stations using the travel speed between departure and destination. First, the travel speed is compared with the average operating speed of ordinary railways. If the travel speed exceeds the average operating speed of ordinary railways, it can be accurately determined that the traveler has chosen railway travel; conversely, if the travel speed is lower than the average operating speed of ordinary railways, the travel mode is identified as road travel. Furthermore, this method further distinguishes between high-speed rail travel and ordinary rail travel: travel data with a travel speed exceeding the average operating speed of high-speed rail is identified as high-speed rail travel; while travel data with a travel speed lower than the average operating speed of high-speed rail is identified as ordinary rail travel. It is important to note that the average operating speed of ordinary and high-speed railways is not fixed but is derived by calculating the actual length of the railway line between departure and destination and the average arrival time. This speed calculation method based on actual operating data can accurately reflect the real operating conditions of different railway lines, providing strong support for accurate classification of travel modes.

[0067] Step 3 is the identification of integrated transportation corridors. First, the bidirectional A* algorithm is used to allocate passenger flow to the road network, and high-volume road sections are merged to identify integrated transportation corridors. This method covers two core aspects: passenger flow allocation and corridor identification.

[0068] ① Passenger Flow Allocation: This method allocates passenger flow based on travelers' route selection behavior, using a bidirectional A* algorithm to rationally distribute OD data across the road network. Specifically, railway travelers typically tend to choose the shortest path, while road travelers randomly select from multiple alternative paths. The bidirectional A* algorithm, as a highly efficient path search algorithm, combines the advantages of best-first search and Dijkstra's algorithm, significantly improving search efficiency. The calculation method is expressed as follows:

[0069] f(n)=g(n)+h(n) (3)

[0070] In the formula, f(n) is the total estimated cost used to evaluate the current node n; g(n) is the actual cost from the starting point to the current node n; and h(n) represents the estimated cost from the current node n to the target node.

[0071] The bidirectional A* algorithm increases path search efficiency by simultaneously searching from both the origin and destination, making it suitable for the complex road network conditions presented in this paper. It effectively reduces the search space by searching from both sides simultaneously, and the entire path is obtained when both search processes meet at a certain node. In the K-shortening Path Search algorithm, K distinct paths are found by progressively deleting edges from the found shortest path and recalculating new shortest paths. Combining these two algorithms can improve path search efficiency in large-scale networks.

[0072] The search process of the bidirectional A* algorithm is as follows:

[0073] Step 1: Create two open lists. Open list 1 searches for paths from the origin to the destination, and open list 2 searches for paths from the destination to the origin. Initialize the total estimated cost, actual cost, and projected cost for the origin and destination. Add the origin to open list 1 and the destination to open list 2.

[0074] Step 2: Extract the node with the smallest total estimated cost from the two open lists and mark it as node O (originating point search) and node D (destination search); when the two nodes overlap to become the same node, the path search is considered complete; otherwise, continue searching for the neighboring nodes of the two nodes; calculate the total estimated cost of all neighboring nodes of node O and the total estimated cost of all neighboring nodes of node D, and add them to development list 1 and development list 2 respectively.

[0075] Step 3: After the path search is completed, merge the two search paths to reverse the process and construct the complete path, outputting the shortest path information.

[0076] Step 4: First, delete one edge from the shortest path in the traffic network, and repeat steps 1, 2, and 3 to calculate the initial shortest path.

[0077] Step 5: Repeat step 4 until all edges in the shortest path have been deleted. Compare all the temporary shortest paths and select the shortest one as the second shortest path.

[0078] Step 6: When K≥3, pair all edges in the first (K-1) shortest paths, deleting one edge pair at a time to calculate the temporary shortest path, until all edge pairs have been deleted. Compare all the obtained temporary shortest paths, and the shortest path is the Kth shortest path. This paper selects the first 3 shortest paths for passenger flow allocation.

[0079] In this embodiment, it is assumed that railway passenger flow follows the shortest path, and the passenger flow allocation is completed accordingly. For road travel, passenger flow allocation is completed based on the probability of path selection. The passenger flow on the k-th path... The calculation method is as follows:

[0080]

[0081] In the formula, This represents the travel time cost of the k-th highway route; v1 represents the highway mileage of the k-th highway route; v1 represents the average speed of the highway. v1 represents the mileage of the first-class highway along the k-th highway route; v2 represents the average speed along the first-class highway. q represents the probability that the k-th path is chosen; od This represents the total highway passenger traffic between OD pairs.

[0082] ② Identification of Integrated Transportation Corridors: After allocating passenger flow among different modes of transportation, railway and highway lines are segmented based on the administrative boundaries of prefecture-level cities. Through precise segmentation, railway and highway lines are divided into several segments, each located within a specific prefecture-level city's administrative region. This segmentation provides clear geographical boundaries for subsequent transport volume statistics and transportation corridor identification. The average transport volume of each segment of the divided railway and highway lines is calculated. Based on this, criteria for identifying railway and highway transportation corridor segments are set at 5,000 and 20,000 passenger trips, respectively. By comparing the average transport volume of each line segment with these criteria, railway and highway transportation corridor segments are identified. This identification process ensures the scientific division of transportation corridors and lays the foundation for subsequent transport efficiency assessment. The average passenger flow results of each mode of transportation are summed to obtain the average passenger flow of each segment of the integrated transportation corridor. During the calculation, segments of a single mode of transportation are excluded to ensure the integrity and representativeness of the integrated transportation corridor. In this way, the average passenger flow in each section of the integrated transportation corridor can more accurately reflect the actual transportation demand and capacity of the region.

[0083] D i.j =n j,t +n j,r (6)

[0084] T i,j =n j,t ×d j,t +n j,r ×d j,r (7)

[0085] In the formula, D i,j T represents the average passenger flow of the i-th integrated transportation corridor on the j-th segment; i,j Let n be the passenger turnover of the i-th integrated transportation corridor in the j-th segment; j,t d represents the average railway passenger volume in section j; j,t Let n be the average railway line length of segment j; j,r d represents the average highway passenger traffic volume in section j; j,t is the average length of the highway in section j; m is the total number of sections.

[0086] To more clearly understand the purpose, features and advantages of this disclosure, in this embodiment, the proposed integrated transportation corridor identification algorithm was verified by mobile phone information data collected in a certain area. Examples of the data used are shown in Table 1.

[0087] Table 1 Mobile Signaling Data Information

[0088]

[0089] First, mobile signaling data from September 28, 2021, was used as sample data for a typical workday for analysis and data preprocessing to verify the performance of the proposed scheme. The intercity trip volume for all OD pairs was found to be 3.3055 million trips. Subsequently, different travel modes were categorized, and the daily intercity trip volume for each mode was calculated. The results show that intercity trips by rail accounted for 599,300 trips, or 18.13%; while intercity trips by road accounted for 2.7062 million trips, or 81.87%.

[0090] After identification, short-distance rail passenger traffic is mainly concentrated within the metropolitan area centered on a certain city in the north, forming a dense network of commuter transport lines. These lines include rail connections between this city and cities in Tianjin, Zhuozhou, Gucheng, and Sanming. The high passenger volume on these lines reflects the strong attraction of this core city to the surrounding areas, as well as the growing commuter demand within the region. Long-distance passenger traffic is mainly distributed between major cities in the regional urban cluster, such as the railway lines between this city and cities in Baoding, Shijiazhuang, Handan, Hengyang, and Cangzhou, and between Zhangjiakou and cities in Beijing, Tangshan, and Qinhuangdao. These lines constitute the main rail transport corridors of this regional urban cluster, carrying a large amount of long-distance passenger traffic and inter-regional economic ties. The section from this city to the nearest first segment has the highest traffic volume, encompassing various types of passenger traffic, including commuter and long-distance transit passengers, fully demonstrating the status of this city as a core city and its key role in the regional transportation network. For example, the average railway passenger volume on the section of the railway transport corridor from Beimou City to Baoding City reached 71,487 passenger trips, with an average railway turnover of 10.9568 million passenger-kilometers on weekdays and 13.0634 million passenger-kilometers on holidays. The distribution of highway transport corridors is basically similar to that of railway transport corridors, but due to the flexibility of highway transport, it provides more convenient routes between adjacent prefecture-level cities and has a wider coverage. For example, the passenger volume on highways between Hengcheng City and Xingcheng City, and between Tiancheng City and Cangzhou City, is much greater than that on railways. The average passenger volume on the highway transport corridor between Hengcheng City and Xingcheng City is 28,853 passenger trips, while the average passenger volume on the railway transport corridor in the same section is only 12,000 passenger trips. The spatial layout of the comprehensive transport corridors within this urban agglomeration verifies the effectiveness of the adopted identification algorithm.

[0091] Example 3

[0092] This embodiment 3 provides a non-transitory computer-readable storage medium for storing computer instructions. When executed by a processor, the computer instructions implement the urban agglomeration integrated transportation corridor identification method based on mobile phone signaling data as described above. The method includes:

[0093] Clean and speed-calculate mobile signaling data, remove abnormal trajectories, and form reliable intercity travel origin-destination (OD) samples.

[0094] Using the POI of a station as an anchor point and combining it with the movement speed threshold, each OD is accurately divided into rail or road travel modes;

[0095] For different modes of travel, the bidirectional A* gradually shortening path algorithm is used on the real road network to allocate passenger flow, merge high-volume sections, and achieve quantitative identification of integrated transportation corridors.

[0096] Example 4

[0097] This embodiment 4 provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, and the memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute the urban agglomeration integrated transportation corridor identification method based on mobile phone signaling data as described above. The method includes:

[0098] Clean and speed-calculate mobile signaling data, remove abnormal trajectories, and form reliable intercity travel origin-destination (OD) samples.

[0099] Using the POI of a station as an anchor point and combining it with the movement speed threshold, each OD is accurately divided into rail or road travel modes;

[0100] For different modes of travel, the bidirectional A* gradually shortening path algorithm is used on the real road network to allocate passenger flow, merge high-volume sections, and achieve quantitative identification of integrated transportation corridors.

[0101] Example 5

[0102] This embodiment 5 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the urban agglomeration integrated transportation corridor identification method based on mobile phone signaling data as described above. The method includes:

[0103] Clean and speed-calculate mobile signaling data, remove abnormal trajectories, and form reliable intercity travel origin-destination (OD) samples.

[0104] Using the POI of a station as an anchor point and combining it with the movement speed threshold, each OD is accurately divided into rail or road travel modes;

[0105] For different modes of travel, the bidirectional A* gradually shortening path algorithm is used on the real road network to allocate passenger flow, merge high-volume sections, and achieve quantitative identification of integrated transportation corridors.

[0106] In summary, this invention provides a method for quantitative identification of integrated transportation corridors based on mobile phone signaling data, aiming to solve the technical challenge of accurately quantifying integrated transportation corridors within urban agglomerations. First, the mobile phone signaling data is cleaned and speed calculated to remove abnormal trajectories, obtaining intercity travel origin-destination (OD) samples. Second, using station points of interest (POIs) as anchor points and combining them with travel speed thresholds, the OD samples are accurately classified into rail or road travel modes. Finally, for different travel modes, passenger flow is allocated on a real road network using a bidirectional A* progressively shorter path algorithm, merging high-flow sections to achieve quantitative identification of integrated transportation corridors.

[0107] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative effort should be included within the scope of protection of the present invention.

Claims

1. A method for identifying integrated transportation corridors in urban agglomerations based on mobile phone signaling data, characterized in that, include: Clean and speed-calculate mobile signaling data, remove abnormal trajectories, and form reliable intercity travel origin-destination (OD) samples. Using the POI of a station as an anchor point and combining it with the movement speed threshold, each OD is accurately divided into rail or road travel modes; For different modes of travel, the bidirectional A* gradually shortening path algorithm is used on the real road network to allocate passenger flow, merge high-volume sections, and achieve quantitative identification of integrated transportation corridors.

2. The method for identifying urban agglomeration integrated transportation corridors based on mobile phone signaling data according to claim 1, characterized in that, The mobile signaling data was cleaned by removing drift data and ping-pong effect abnormal trajectories through timestamp conversion and velocity calculation, while retaining intercity travel OD samples. Road network data and railway station POI data of the study area were imported and spatial alignment and visualization verification were performed.

3. The method for identifying urban agglomeration integrated transportation corridors based on mobile phone signaling data according to claim 2, characterized in that, Spatial matching is performed using latitude and longitude coordinates from mobile phone signaling data and POIs of railway stations. For special travel chains that arrive at railway stations after crossing administrative divisions, the entire journey is directly and forcibly classified as railway to eliminate boundary misjudgments. Speed ​​thresholds are used for secondary discrimination of ODs around the stations. Thus, each OD is given a precise label of highway, ordinary railway or high-speed railway, and the proportion distribution of the three travel modes is finally output.

4. The method for identifying urban agglomeration integrated transportation corridors based on mobile phone signaling data according to claim 3, characterized in that, The origin-destination (OD) traffic data with completed travel mode marking is imported into the real road network, and passenger flow is allocated based on differences in traveler behavior. After allocation, the railway and highway lines are divided into several segments based on the administrative boundaries of prefecture-level cities. The average transport volume of each segment is calculated separately, and high-volume segments are selected. The passenger flow of the segments of the two modes is weighted by mileage and summed. Segments containing only a single mode of transportation are eliminated, and the remaining high-volume segments are merged to form a comprehensive transportation corridor, thereby obtaining a quantitative identification result that can accurately reflect the actual transportation demand of the urban agglomeration.

5. The method for identifying urban agglomeration integrated transportation corridors based on mobile phone signaling data according to claim 4, characterized in that, The passenger flow allocation based on differences in traveler behavior includes: for railway passenger flow, the shortest path is directly searched using the bidirectional A* algorithm, while for highway passenger flow, the K-step shortening path technique is used. First, the shortest path is obtained using the bidirectional A* algorithm, and then the second and third shortest paths are generated sequentially through edge deletion and edge pair deletion strategies, thereby ensuring computational efficiency while covering the random route selection behavior of highway travelers. Subsequently, the highway route selection probability is calculated by weighting the travel time cost and grade mileage of each path, and the OD traffic is allocated to the corresponding road segments proportionally.

6. The method for identifying urban agglomeration integrated transportation corridors based on mobile phone signaling data according to claim 1, characterized in that, The search process of the bidirectional A* algorithm is as follows: Step 1: Create two open lists. Open list 1 searches for paths from the origin to the destination, and open list 2 searches for paths from the destination to the origin. Initialize the total estimated cost, actual cost, and projected cost for the origin and destination. Add the origin to open list 1 and the destination to open list 2. Step 2: Extract the node with the smallest total estimated cost from the two open lists and mark it as node O and node D respectively; when the two nodes overlap to become the same node, the path search is considered complete; otherwise, continue to search the neighboring nodes of the two nodes; calculate the total estimated cost of all neighboring nodes of node O and the total estimated cost of all neighboring nodes of node D, and add them to open list 1 and open list 2 respectively. Step 3: After the path search is completed, merge the two search paths to reverse the process and construct the complete path, then output the shortest path information; Step 4: First, delete one edge from the shortest path in the traffic network, and repeat steps 1, 2, and 3 to calculate the initial secondary shortest path; Step 5: Repeat step 4 until all edges in the shortest path have been deleted. Compare all the temporary shortest paths and select the shortest one as the second shortest path. Step 6: When K≥3, pair all edges in the first (K-1) shortest paths, delete one edge pair at a time to calculate the temporary shortest path, until all edge pairs have been deleted, compare all the obtained temporary shortest paths, and the shortest path is the Kth shortest path.

7. A comprehensive urban agglomeration transportation corridor identification system based on mobile phone signaling data, characterized in that, include: The preprocessing module is used to clean and calculate the speed of mobile phone signaling data, remove abnormal trajectories, and form reliable intercity travel OD samples; The segmentation module is used to accurately classify each OD (Original Destination) into rail or road travel modes, using the site POI as the anchor point and combining it with the movement speed threshold. The identification module is used to allocate passenger flow on a real road network using the bidirectional A* gradually shortening path algorithm for different modes of travel, merge high-volume sections, and achieve quantitative identification of integrated transportation corridors.

8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the urban agglomeration integrated transportation corridor identification method based on mobile phone signaling data as described in any one of claims 1-6.

9. A computer device, characterized in that, The method includes a memory and a processor, the processor and the memory communicating with each other, the memory storing program instructions executable by the processor, and the processor calling the program instructions to execute the urban agglomeration integrated transportation channel identification method based on mobile phone signaling data as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the urban agglomeration integrated transportation corridor identification method based on mobile phone signaling data as described in any one of claims 1-6.

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