Online car-hailing travel substitution mode recognition and carbon effect space-time measurement method

Abstract

The invention provides an online car-hailing travel substitution mode recognition and carbon effect space-time measurement method, which comprises the following steps of: acquiring travel cost information of online car-hailing and public transport, comparing travel costs of two types of traffic travel modes under the same travel, comprehensively applying a demand elasticity theory and a utility maximization principle in microeconomics, and calculating the travel substitution mode of the online car-hailing and the carbon effect space-time measurement of the online car-hailing and the carbon effect space-time measurement of the online car-hailing. The method comprises the following steps: constructing a potential substitution relationship identification model between online hailed cars and public transport, accurately identifying whether each online hailed car travel potentially substitutes the public transport by analyzing the sensitivity of a travel cost threshold value, fusing the concepts of a COPERT carbon emission model and a space-time cube, and taking any time as granularity to identify the potential substitution relationship between the online hailed cars and the public transport. And the substitution rates of different time scenes are summarized to a space level, so that the corresponding carbon emission can be found at any time and in any space range, and the space-time measurement of the carbon effect of the online car-hailing travel mode is realized. The method is used for solving the problem of space-time uncertainty of the carbon effect of the online car-hailing travel substitution mode.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon transportation technology, and in particular to a spatiotemporal measurement method for carbon effects. Background Art

[0002] Scientifically measuring the carbon effect of online ride-hailing travel mode is an important part of promoting carbon emission reduction in the field of urban transportation. However, the uncertainty of residents' travel behavior and the complex relationship between it and the dynamics of the geographical background have become difficulties in evaluating the carbon effect of online ride-hailing travel mode. On the one hand, the flexibility of online ride-hailing can complement the public transportation system and effectively improve vehicle utilization through the carpooling model, thereby reducing carbon emissions. On the other hand, online ride-hailing may increase carbon emissions by replacing public transportation. Driven by both practical needs and theoretical origins, how to scientifically, quantitatively and accurately evaluate the carbon effect of online ride-hailing travel alternative modes is a key scientific issue that needs to be urgently solved in the current and future construction of urban sustainable transportation.

[0003] Existing studies mainly identify the relationship between online ride-hailing travel patterns and carbon effects from the individual non-aggregate level and the spatial aggregation level. The former is based on questionnaires or simulations to make assumptions and simulations on the alternative scenarios of online ride-hailing travel, and tends to be theoretical analysis. For example, through the researcher's questioning method. This is used to measure the degree of substitution of online ride-hailing for public transportation. The spatial aggregation level is based on travel big data, and compares the travel costs of the two travel modes under the same trip through travel simulation. However, due to ignoring the complexity of actual transportation travel, such as the time spent walking to public transportation stations, traffic operating hours, congestion, number of transfers, trip fares, etc., the substitution effect may be seriously overestimated. In addition, citizens' travel behavior has strong spatiotemporal uncertainty. Only focusing on a single, static spatiotemporal behavior study, and interpreting behavior patterns without considering the spatiotemporal uncertainty of the geographical background, not only cannot fully describe travel behavior at a finer time granularity, but also cannot reveal the dynamic change process of its carbon effect, and even lead to contradictory conclusions. In summary, these methods cannot fully capture the temporal dynamics of travel patterns and the spatial heterogeneity of behavioral activities. Temporal dynamics: This refers to the property that travel patterns change over time. For example, residents' travel patterns may be different on weekdays and weekends, or may change at different times of the day (such as morning and evening rush hours). This dynamic nature needs to be captured through time series analysis or dynamic models to better understand the changing trends and patterns of travel demand. Spatial heterogeneity: This refers to the fact that in different geographical locations, residents' travel behaviors and travel patterns may vary significantly. This difference may be affected by a variety of factors, including land use patterns, the distribution of transportation infrastructure, and the socioeconomic characteristics of residents. For example, travel patterns in the central area of ​​a city may be very different from those in suburban or rural areas. It is also impossible to answer exactly when, where, and to what extent online ride-hailing will replace public transportation, making it difficult to accurately measure its actual carbon effects. Summary of the invention

[0004] In view of the technical problem that the traditional online car-hailing travel alternative mode identification research cannot fully capture the temporal dynamics of travel modes and the spatial heterogeneity of behavioral activities, making it difficult to accurately measure its actual carbon effect, the present invention proposes a method for identifying online car-hailing travel alternative modes and measuring its carbon effect in time and space. The present invention comprehensively applies the demand elasticity theory and the principle of utility maximization, and incorporates the concept of space-time cube to quantify the substitution relationship between online car-hailing and public transportation under different time scenarios and summarize it to the spatial level, so that the corresponding carbon emissions can be found in any time and space range.

[0005] In order to achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] A method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space, including:

[0007] Step 1: Obtain the trip cost information of online ride-hailing and public transportation;

[0008] Step 2: Based on the demand elasticity theory and utility maximization principle in microeconomics, combined with the travel cost information of online ride-hailing and public transportation, a model for identifying the potential substitution relationship between online ride-hailing and public transportation is constructed;

[0009] Step 3: Conduct a threshold sensitivity analysis on the travel cost information of online ride-hailing and public transportation based on the substitution relationship identification framework, determine the substitution relationship criterion based on the threshold sensitivity analysis results, and calculate the final substitution rate of online ride-hailing for public transportation;

[0010] Step 4: Based on the final substitution rate of online ride-hailing for public transportation, the COPERT motor vehicle carbon emission model is used to calculate the carbon emissions generated by the behavior of online ride-hailing replacing public transportation;

[0011] Step five: construct a space-time cube based on the final substitution rate of online ride-hailing for public transportation and the carbon emissions generated by the behavior of online ride-hailing replacing public transportation, so as to achieve the space-time measurement of the carbon effect of online ride-hailing as an alternative travel mode.

[0012] The step one specifically includes: obtaining online car-hailing order data and calculating online car-hailing travel cost information; and obtaining corresponding public transportation travel cost information using an Internet map route planning service for the online car-hailing order data.

[0013] The online car-hailing order data includes order ID, passenger boarding time, passenger alighting time, passenger boarding location, passenger alighting location, travel distance, travel time, and travel fare; the travel cost information of the online car-hailing and public transportation includes travel time, travel distance, travel fare, and number of transfers, and the travel time includes walking time, riding time, and time required for transfers.

[0014] The method for obtaining the corresponding public transportation travel cost information by using the Internet map route planning service is:

[0015] 1) Register as a developer of the Internet Map Path Planning Service Platform and apply for a Web Service Key;

[0016] 2) Set the departure time, travel mode, and coordinate reference according to the time and location of passengers getting on and off the bus in the online car-hailing order data;

[0017] 3) Send route planning requests in batches to the Internet map route planning service platform to obtain travel cost information for taking public transportation during the corresponding time period and under the same itinerary as the online car-hailing service.

[0018] The potential substitution relationship identification model between online car-hailing and public transportation is:

[0019]

[0020] Among them, S1 is a dataset that meets the conditions of temporal and spatial coverage, S2 is a dataset that meets the conditions of the number of transfers, S3 is a dataset that meets the conditions of travel time, and S4 is a dataset that meets the conditions of travel cost; a is a single online car-hailing trip, S is the set of all online car-hailing trips; O a and D a They are the boarding and alighting locations, B n (m) is the walking distance threshold between the online ride-hailing vehicle boarding and alighting locations and public transportation stations; B t is the number of transfers by public transportation, τ t is the transfer threshold; T R is the travel time of taking an online ride-hailing car, T B is the time of taking public transportation for the same trip, τ T is the threshold of travel time difference; P R is the trip cost of taking an online ride-hailing car, P B is the cost of taking public transportation for the same trip, τ P is the threshold of trip cost difference.

[0021] The method for threshold sensitivity analysis described in step three is: setting a baseline threshold corresponding to the four-layer structure, changing one of the thresholds among the spatiotemporal coverage, number of transfers, travel time and travel fare in the four-layer structure respectively, while keeping the other thresholds unchanged, calculating the substitution rate of online ride-hailing for public transportation, and analyzing the impact of each threshold on the substitution rate, determining the final threshold standard of the substitution relationship according to the change in the substitution rate, determining the final substitution relationship criterion according to the final threshold standard, taking the online ride-hailing trips that meet the final substitution relationship criterion as online ride-hailing replacement trips, and recording the number of online ride-hailing replacement trips and the trip information of online ride-hailing that has the potential to replace public transportation.

[0022] The method for calculating the substitution rate of online ride-hailing for public transportation is:

[0023] 1) Divide the road network in the study area into several units;

[0024] 2) Define the replacement rate as:

[0025] in, represents the replacement rate of unit k in period j, Q kj is the number of online car-hailing trips in unit k during time period j, S kj Represents the number of all online car-hailing trips in unit k during time period j.

[0026] The method for calculating the carbon emissions generated by the behavior of online ride-hailing travel replacing public transportation described in step 4 is: based on the final substitution rate of online ride-hailing for public transportation, trace back the trip information of online ride-hailing vehicles with the potential to replace public transportation corresponding to each online ride-hailing replacement trip i, and combine the trip information of online ride-hailing vehicles with the potential to replace public transportation, and calculate the emission factor according to the COPERT motor vehicle carbon emission model:

[0027] The carbon emissions generated by online ride-hailing trips replacing public transportation are calculated based on emission factors and online ride-hailing trip information that has the potential to replace public transportation. By screening different time periods, the carbon emissions of a unit k in any time period j are obtained.

[0028] The method for calculating the emission factor is:

[0029]

[0030] In the formula, E p,i is the emission factor of pollutant p generated by the online car-hailing alternative trip i, v i is the average speed of the online-hailing car corresponding to the online-hailing car replacement trip i; α p is the coefficient of the average speed square term; β p is the coefficient of the average velocity term; γ p is the coefficient of the constant term; δ p is the coefficient of the reciprocal term of the average speed; ε p is the normalization coefficient of the average speed square term; ζ p is the normalization coefficient of the average speed term; η p is the normalized coefficient of the constant term;

[0031] The formula for calculating carbon emissions is:

[0032] Q p,i =E p,i ×d i

[0033] In the formula, Q p,i Carbon emissions generated by replacing trips i with online ride-hailing services, d i is the driving distance corresponding to the alternative trip i of the online car-hailing service.

[0034] The method for constructing the space-time cube is as follows: first, the centroid coordinates of each unit are extracted, and a two-dimensional plane coordinate (x, y) is used to express the real-world spatial position, and the departure time t in the online car-hailing replacement trip is regarded as the third dimension to express the dynamic change of the spatial position over time, forming a space-time cube. Secondly, the unit replacement rate and the corresponding carbon emissions of the unit are superimposed in a time-sorted manner, and the data with space-time attributes are mapped into the space-time cube.

[0035] Beneficial effects of the present invention:

[0036] (1) The present invention can quantify the substitution relationship between online ride-hailing and public transportation under different time scenarios and summarize it at the spatial level, so that the corresponding carbon emissions can be found in any time and space range, realizing the spatiotemporal measurement of the carbon effect of online ride-hailing travel substitution mode, and solving the spatiotemporal uncertainty problem of the carbon effect of urban residents' travel mode.

[0037] (2) The present invention has a certain degree of universality and can be applied to measure the carbon effects of other travel modes. At the same time, the present invention can provide theoretical and methodological support for carbon emission reduction in the transportation field and provide decision-making basis and application reference for the sustainable development path of cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a schematic flow chart of the method of the present invention.

[0040] Figure 2 It is a model for identifying the potential substitution relationship between online ride-hailing and public transportation. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] A method for identifying online ride-hailing travel substitution patterns and measuring their carbon effects in time and space, such as Figure 1 As shown, the steps include:

[0043] Step 1: Obtain travel cost information for online ride-hailing and public transportation.

[0044] Apply for corresponding online car-hailing order data from the online car-hailing data open platform, where the online car-hailing order data includes order ID, passenger boarding time, passenger alighting time, passenger boarding location, passenger alighting location, travel distance, travel time, and travel fare, as shown in Table 1; calculate the online car-hailing travel cost information; the online car-hailing travel cost information includes travel time, travel distance, and travel fare.

[0045] Table 1 Online car-hailing order data

[0046]

[0047] For each piece of online ride-hailing order data, the Internet map route planning service is used to obtain the travel cost information of taking public transportation (bus or subway) for the same trip within the corresponding time period.

[0048] How to use the Internet map route planning service:

[0049] First, choose a suitable Internet map route planning service platform (such as Amap Open Platform, Baidu Map Open Platform, etc.), register as an Internet map open platform developer, and apply for a Web service API key;

[0050] Secondly, call the API interface provided by the platform through Python, construct the HTTP request URL, and set the starting point (origin), end point (destination), departure city (city), departure date (date), departure time (time), coordinate reference and other optional parameters in the request URL.

[0051] On this basis, we use the Python requests library to send batch GET requests to the constructed URL and receive the returned data. Finally, we extract relevant information including travel time (walking time, ride time, transfer time), travel distance, travel fare, and number of transfers from the returned data to obtain the travel cost information of taking public transportation in the corresponding time period and the same trip. As shown in Table 2, the travel cost information of online car-hailing and public transportation in different trips and different time periods is shown.

[0052] Table 2 Comparison of travel costs between online ride-hailing and public transportation for the same trip

[0053]

[0054] Step 2: Based on the demand elasticity theory and utility maximization principle in microeconomics, and combined with the travel cost information of online ride-hailing and public transportation, a model for identifying the potential substitution relationship between online ride-hailing and public transportation is constructed.

[0055] like Figure 2 As shown, the principle is as follows:

[0056] In microeconomics, the difference between substitutes and complements lies in the cross elasticity of demand, which means that the amount of consumer demand will fluctuate with changes in constraints (such as commodity prices, consumer income, etc.). The utility maximization theory describes the utility maximization pursued by consumers after weighing various constraints (such as income levels and commodity prices) when faced with a choice of several consumer goods.

[0057] When residents make transportation decisions, the choice of travel mode is also regarded as a consumption pattern. The so-called alternative travel refers to two travel modes that have the same or similar effects (such as convenience, travel costs, etc.) in meeting the travel needs of residents, so that residents can choose one of them as an alternative travel plan. According to the demand elasticity theory and utility maximization theory in microeconomics, for transportation decisions, residents will always make rational choices on the utility of alternative travel plans based on constraints such as convenience, time cost and travel expenses in order to maximize benefits. By incorporating this theory, more reasonable judgment and measurement criteria can be provided for the identification of travel substitution relationships. Accordingly, the present invention sets four levels for determining the potential substitution effect between online car-hailing and public transportation. Only online car-hailing trips that meet these four levels at the same time are regarded as alternative trips for public transportation. The four-layer structure mainly includes:

[0058] 1) Spatial and temporal coverage: The pick-up and drop-off times in online ride-hailing orders are all within the public transportation operation hours, and the pick-up and drop-off locations of online ride-hailing orders are within an appropriate walking distance from public transportation stations (bus stops and subway station entrances and exits) for the same trip. The dataset that meets this condition is recorded as S1;

[0059] 2) Number of transfers: Taking public transportation on the same trip requires only a small number of transfers. The data that meets this condition is recorded as set S2;

[0060] 3) Travel time: The travel time costs of the two types of travel modes for the same trip are similar. The data that meets this condition is recorded as set S3;

[0061] 4) Trip cost: The difference in trip costs between the two types of travel modes for the same trip is small. The data that meets this condition is recorded as set S4;

[0062]

[0063] Where a is a single online ride-hailing trip (one online ride-hailing trip), S is the set of all online ride-hailing trips; O a and D a They are the boarding and alighting locations, B n (m) is the walking distance threshold between the online ride-hailing vehicle boarding and alighting locations and public transportation stations; B t is the number of transfers by public transportation, τ tis the transfer threshold; T R is the travel time of taking an online ride-hailing car, T B is the time of taking public transportation for the same trip, τ T is the threshold of travel time difference; P R is the trip cost of taking an online ride-hailing car, P B is the cost of taking public transportation for the same trip, τ P is the threshold of trip cost difference.

[0064] Step three, conduct a threshold sensitivity analysis on the travel cost information of online ride-hailing and public transportation based on the substitution relationship identification framework, determine the substitution relationship criterion based on the results of the threshold sensitivity analysis, and calculate the final substitution rate of online ride-hailing for public transportation.

[0065] The calculation method of the substitution rate of online ride-hailing for public transportation is:

[0066] 1) Divide the road network in the study area into several block units or regular grids;

[0067] 2) The replacement rate is defined as:

[0068] Where: R kj represents the replacement rate of unit k in period j, Q kj is the number of online car-hailing alternative trips in unit k in time period j (a data set that satisfies the above four-layer structure), S kj Represents the number of all online car-hailing trips in unit k during time period j.

[0069] The threshold sensitivity analysis specifically sets a baseline threshold value corresponding to the four-layer structure, changes one of the thresholds among the spatiotemporal coverage, number of transfers, travel time and travel fare in the four-layer structure respectively, while keeping the other thresholds unchanged, calculates the substitution rate of online ride-hailing for public transportation, and analyzes the impact of each threshold on the substitution rate, determines the final threshold standard of the substitution relationship according to the change of the substitution rate, determines the final substitution relationship criterion according to the final threshold standard, and uses the online ride-hailing trips that meet the final substitution relationship criterion as substitute trips for public transportation, namely, online ride-hailing replacement trips, records the number of online ride-hailing replacement trips and the trip information of online ride-hailing trips with the potential to replace public transportation.

[0070] The present invention takes Chengdu online car-hailing order data as an example. For the first layer of the four-layer structure, when the online car-hailing pick-up and drop-off location is within the walking range threshold B of the public transportation station, n When (m)≤5min, only 9.16% of online car-hailing trips are likely to be substituted by public transportation; when B n (m)≤15min, the replacement rate is as high as 80%. n (m) changes, and it is found that when Bn When (m)≤10min, 48.80% of online ride-hailing trips have the potential to replace public transportation, which is more in line with the actual situation. After adding the online ride-hailing trips that meet the first level to the second level, it is found that when the transfer threshold τ t = 0, only 27.21% of public transportation trips can satisfy passengers' need to reach their destinations without any transfers; τ t ≤1, the replacement rate is higher than 42%; for the third and fourth layers, when the travel time difference threshold τ T ≤25min, and the trip fare difference threshold τ P When the price is ≤20 yuan, the substitution rate shows an obvious inflection point, at which time the substitution rate remains below 30%. For online car-hailing trips between 06:00 and 23:59, the substitution relationship criteria determined according to the final threshold standard are as follows:

[0071] 1) Walking distance between online ride-hailing boarding and alighting locations and public transportation stops B n (m) ≤ 10 minutes;

[0072] 2) The number of transfers when taking public transportation τ t ≤1 time;

[0073] 3) Travel time difference threshold τ between two types of travel modes T ≤25 minutes;

[0074] 4) The travel cost difference threshold τ between the two travel modes P ≤20 Yuan.

[0075] Only online ride-hailing trips that simultaneously meet the above substitution relationship criteria are considered as alternative trips to public transportation. The number of online ride-hailing trips that have the potential to replace public transportation is recorded as Q. kj , according to the number of online car-hailing trips that satisfy the substitution relationship criterion and have the potential to replace public transportation is Q kj Calculate the final substitution rate of online ride-hailing for public transportation.

[0076] Step 4: Use the COPERT motor vehicle carbon emission model based on the final substitution rate of online ride-hailing for public transportation to calculate the carbon emissions generated by online ride-hailing replacing public transportation.

[0077] According to the final substitution rate of online-hailing vehicles for public transportation, the trip information of online-hailing vehicles with the potential to replace public transportation corresponding to each online-hailing vehicle replacement trip i is traced back. Combined with the trip information of online-hailing vehicles with the potential to replace public transportation, including the travel time and distance of online-hailing vehicles with the potential to replace public transportation, the emission factor is calculated according to the COPERT motor vehicle carbon emission model:

[0078]

[0079] In the formula, E p,i is the emission factor of pollutant p generated by the online car-hailing alternative trip i, v i is the average speed of the online-hailing car corresponding to the online-hailing car replacement trip i; α p is the coefficient of the average speed square term; β p is the coefficient of the average velocity term; γ p is the coefficient of the constant term; δ p is the coefficient of the reciprocal term of the average speed; ε p is the normalization coefficient of the average speed square term; ζ p is the normalization coefficient of the average speed term; η p is the normalized coefficient of the constant term, and the parameter values ​​of each coefficient are shown in Table 3.

[0080] Calculate the carbon emissions generated by using ride-hailing to replace public transportation:

[0081] Q p,i =E p,i ×d i

[0082] In the formula, Q p,i is the carbon emissions generated by the online car-hailing alternative trip i, d i For the driving distance corresponding to the online ride-hailing alternative trip i, the carbon emissions of a unit k in any j time period can be obtained by screening different time periods.

[0083] Table 3 COPERT emission model parameters

[0084]

[0085]

[0086] Step five: construct a space-time cube based on the final substitution rate of online ride-hailing for public transportation and the carbon emissions generated by the behavior of online ride-hailing replacing public transportation, so as to achieve the space-time measurement of the carbon effect of online ride-hailing as an alternative travel mode.

[0087] Specifically:

[0088] First, the centroid coordinates of each research unit are extracted, and a two-dimensional plane coordinate (x, y) is used to express the real-world spatial position. The departure time (t) in the alternative journey is regarded as the third dimension to express the dynamic change of the spatial position over time, forming a space-time cube. Secondly, the unit replacement rate and the corresponding carbon emissions of the unit are superimposed in time order. In this way, data with spatiotemporal attributes can be mapped to the cube, and the corresponding carbon emissions can be found in any time and space range. The space-time cube can provide a multi-dimensional data view to help us better understand the changes in the substitution rate of online ride-hailing for public transportation and the carbon emissions it generates in time and space, thereby realizing the spatiotemporal measurement of the carbon effect of online ride-hailing alternative travel modes. The space-time cube visualization in the present invention can be achieved through ArcGIS Pro software.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying online car-hailing travel substitution patterns and measuring their carbon effect in time and space, characterized in that: include: Step 1: Obtain the trip cost information of online ride-hailing and public transportation; Step 2: Based on the demand elasticity theory and utility maximization principle in microeconomics, combined with the travel cost information of online ride-hailing and public transportation, a model for identifying the potential substitution relationship between online ride-hailing and public transportation is constructed; Step 3: Conduct a threshold sensitivity analysis on the travel cost information of online ride-hailing and public transportation based on the substitution relationship identification framework, determine the substitution relationship criterion based on the threshold sensitivity analysis results, and calculate the final substitution rate of online ride-hailing for public transportation; Step 4: Based on the final substitution rate of online ride-hailing for public transportation, the COPERT motor vehicle carbon emission model is used to calculate the carbon emissions generated by the behavior of online ride-hailing replacing public transportation; Step five: construct a space-time cube based on the final substitution rate of online ride-hailing for public transportation and the carbon emissions generated by the behavior of online ride-hailing replacing public transportation, so as to achieve the space-time measurement of the carbon effect of online ride-hailing as an alternative travel mode.

2. The method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space according to claim 1 is characterized in that: The step one specifically includes: obtaining online car-hailing order data and calculating online car-hailing travel cost information; and obtaining corresponding public transportation travel cost information using an Internet map route planning service for the online car-hailing order data.

3. The method for identifying alternative travel modes of online ride-hailing and measuring their carbon effects in time and space according to claim 2 is characterized in that: The online car-hailing order data includes order ID, passenger boarding time, passenger alighting time, passenger boarding location, passenger alighting location, travel distance, travel time, and travel fare; the travel cost information of the online car-hailing and public transportation includes travel time, travel distance, travel fare, and number of transfers, and the travel time includes walking time, riding time, and time required for transfers.

4. The method for identifying alternative modes of online car-hailing travel and measuring its carbon effect in time and space according to claim 2 or 3, characterized in that: The method for obtaining the corresponding public transportation travel cost information by using the Internet map route planning service is: 1) Register as a developer of the Internet Map Path Planning Service Platform and apply for a Web Service Key; 2) Set the departure time, travel mode, and coordinate reference according to the time and location of passengers getting on and off the bus in the online car-hailing order data; 3) Send route planning requests in batches to the Internet map route planning service platform to obtain travel cost information for taking public transportation during the corresponding time period and under the same itinerary as the online car-hailing service.

5. The method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space according to claim 4 is characterized in that: The potential substitution relationship identification model between online car-hailing and public transportation is: Among them, S1 is a dataset that meets the conditions of temporal and spatial coverage, S2 is a dataset that meets the conditions of the number of transfers, S3 is a dataset that meets the conditions of travel time, and S4 is a dataset that meets the conditions of travel cost; a is a single online car-hailing trip, S is the set of all online car-hailing trips; O a and D a They are the boarding and alighting locations, B n (m) is the walking distance threshold between the online ride-hailing vehicle boarding and alighting locations and public transportation stations; B t is the number of transfers by public transportation, τ t is the transfer threshold; T R is the travel time of taking an online ride-hailing car, T B is the time of taking public transportation for the same trip, τ T is the threshold of travel time difference; P R is the trip cost of taking an online ride-hailing car, P B is the cost of taking public transportation for the same trip, τ P is the threshold of trip cost difference.

6. The method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space according to claim 5 is characterized in that: The method for threshold sensitivity analysis described in step three is: setting a baseline threshold corresponding to the four-layer structure, changing one of the thresholds among the spatiotemporal coverage, number of transfers, travel time and travel fare in the four-layer structure respectively, while keeping the other thresholds unchanged, calculating the substitution rate of online ride-hailing for public transportation, and analyzing the impact of each threshold on the substitution rate, determining the final threshold standard of the substitution relationship according to the change in the substitution rate, determining the final substitution relationship criterion according to the final threshold standard, taking the online ride-hailing trips that meet the final substitution relationship criterion as online ride-hailing replacement trips, and recording the number of online ride-hailing replacement trips and the trip information of online ride-hailing that has the potential to replace public transportation.

7. The method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space according to claim 6 is characterized in that: The method for calculating the substitution rate of online ride-hailing for public transportation is: 1) Divide the road network in the study area into several units; 2) Define the replacement rate as: in, represents the replacement rate of unit k in period j, Q kj is the number of online car-hailing trips in unit k during time period j, S kj Represents the number of all online car-hailing trips in unit k during time period j.

8. The method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space according to claim 7 is characterized in that: The method for calculating the carbon emissions generated by the behavior of online ride-hailing travel replacing public transportation described in step 4 is: based on the final substitution rate of online ride-hailing for public transportation, trace back the trip information of online ride-hailing vehicles with the potential to replace public transportation corresponding to each online ride-hailing replacement trip i, and combine the trip information of online ride-hailing vehicles with the potential to replace public transportation, and calculate the emission factor according to the COPERT motor vehicle carbon emission model: The carbon emissions generated by online ride-hailing trips replacing public transportation are calculated based on emission factors and online ride-hailing trip information that has the potential to replace public transportation. By screening different time periods, the carbon emissions of a unit k in any time period j are obtained.

9. The method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space according to claim 8, characterized in that: The method for calculating the emission factor is: In the formula, E p,i is the emission factor of pollutant p generated by the online car-hailing alternative trip i, v i is the average speed of the online-hailing car corresponding to the online-hailing car replacement trip i; α p is the coefficient of the average speed square term; β p is the coefficient of the average velocity term; γ p is the coefficient of the constant term; δ p is the coefficient of the reciprocal term of the average speed; ε p is the normalization coefficient of the average speed square term; is the normalization coefficient of the average speed term; η p is the normalized coefficient of the constant term; The formula for calculating carbon emissions is: Q p,i =E p,i ×d i In the formula, Q p,i Carbon emissions generated by the ride-hailing alternative trip i, d i is the driving distance corresponding to the alternative trip i of the online car-hailing service.

10. The method for identifying alternative modes of online ride-hailing travel and measuring its carbon effect in time and space according to claim 9, characterized in that: The method for constructing the space-time cube is as follows: first, the centroid coordinates of each unit are extracted, and a two-dimensional plane coordinate (x, y) is used to express the real-world spatial position, and the departure time t in the online car-hailing replacement trip is regarded as the third dimension to express the dynamic change of the spatial position over time, forming a space-time cube. Secondly, the unit replacement rate and the corresponding carbon emissions of the unit are superimposed in a time-sorted manner, and the data with space-time attributes are mapped into the space-time cube.

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