CN112116124A - Auditing method of bus network optimization scheme based on traveler visual angle - Google Patents

Auditing method of bus network optimization scheme based on traveler visual angle Download PDF

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CN112116124A
CN112116124A CN202010790053.3A CN202010790053A CN112116124A CN 112116124 A CN112116124 A CN 112116124A CN 202010790053 A CN202010790053 A CN 202010790053A CN 112116124 A CN112116124 A CN 112116124A
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翁剑成
马思雍
史清帅
郝思佳
李宗典
钱慧敏
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Abstract

The invention discloses an auditing method of a public traffic network optimization scheme based on a traveler visual angle. In the previous evaluation stage, a bus route adjusting influence index BAI based on passenger flow transfer saturation and travel time extension ratio is constructed; and dividing the influence degree into three grades according to the size of the BAI index, evaluating the influence degree of the initial scheme on related travelers, and providing corresponding correction feedback suggestions. In the post-evaluation stage, after the scheme is implemented, the actual passenger flow transfer condition and the trip characteristic change of the frequent passengers in the line are concerned from two levels of the network and the line respectively, and the result of the pre-evaluation and the application effect of the bus network optimization method are tracked and fed back. The invention provides support for realizing the bus network optimization scheme based on the view angle of travelers.

Description

Auditing method of bus network optimization scheme based on traveler visual angle
Technical Field
The invention relates to a public transport network optimization auditing method based on traveler visual angle and using public transport card swiping data, belonging to the field of public transport line optimization and service evaluation.
Background
Under the impact of a plurality of emerging travel modes, the ground public transportation passenger volume of the domestic large city generally shows a descending trend. The method enhances the public transport attraction, improves the matching degree of the public transport supply and demand and the operation efficiency, and is a main research target of the optimization of the public transport network. The current evaluation means of the optimization effect of the public traffic network is insufficient, and when the optimization evaluation of the public traffic network is implemented, due to the lack of sufficient basis and scientific decision support, the current methods mostly pay attention to the optimization method of the network, but pay less attention to the result evaluation after optimization. After the optimization scheme of the intersection network is implemented, the existing evaluation and audit method mainly considers the static indexes of the structure level of the intersection network and is out of line with the actual travel demand of passengers, and the bus route can not be effectively re-optimized until serious negative influence is generated and clear negative feedback of travelers is received.
The invention provides an evaluation method of comprehensive indexes of an urban public transport network with the application number of CN201811167815.3, which relates to the technical field of urban transport operation management.A method for evaluating the comprehensive indexes of the urban public transport network comprises the steps of firstly collecting operation data of the public transport network and calculating the comprehensive evaluation indexes of the public transport network; then determining a range threshold value of the comprehensive evaluation index; determining a bus network structure improvement scheme according to the range threshold of the comprehensive evaluation index; and finally, evaluating the bus network structure improvement scheme by using a bus network evaluation index system. However, the method only evaluates the initial bus network from the aspects of accessibility, reliability, passenger flow matching degree and the like, and a uniform evaluation method is not provided.
The invention discloses a Chinese invention method with application number CN201710092948.8, and relates to a bus GPS data-based method for evaluating the real-time running level of a large-scale urban public transport network, which comprises data preprocessing, map matching, travel time index calculation, stop and stop time index calculation and congestion index calculation. The method is used for evaluating the real-time running level of the public transport network, and the influence on the passenger trip characteristics after the network optimization is not evaluated.
With the development of intelligent traffic information technology, ground public traffic systems generate massive public traffic operation and running data every day, including public traffic GPS data, IC card swiping card data and the like. Taking Beijing as an example, the daily card swiping amount of public transport is about 1700 thousands of people, and the card swiping data records the time and the position of getting on or off the bus of a passenger, riding routes and other important traffic information. Meanwhile, along with the online of a new generation of bus intelligent terminal, the bus card swiping data and the GPS data are transmitted back in real time, so that the data quality and the data timeliness are changed, and the bus running state monitoring data tends to be complete. The increasingly high-quality data conditions provide powerful support for long-term perception of the individual trip characteristics of the bus, and lay a foundation for further integrating the optimization requirements of the bus network, providing comprehensive and quantitative decision support for audit comparison before implementation of the optimization scheme and effect evaluation after implementation of the scheme.
Disclosure of Invention
The invention aims to provide a public traffic network optimization auditing method based on a traveler visual angle, which comprises correction feedback on an initial scheme and tracking feedback on an application effect of the public traffic network optimization method, and provides support for realizing evaluation of the public traffic network optimization scheme based on the traveler visual angle. The evaluation feedback of the two stages starts from the perspective of travelers, the service level of the public traffic network is improved to the maximum extent, and the specific flow is shown in figure 1:
in order to achieve the above object, the present invention adopts the following technical solutions.
A public transport network optimization auditing method based on a traveler visual angle is characterized by comprising the following steps:
step 1: corrective feedback based on pre-adjustment evaluation of net optimization schemes
Aiming at an initial optimization scheme, the conditions of passenger flow transfer and travel time extension brought by travelers are probably given through adjustment of mass public transport historical travel data prediction lines, a quantitative correction feedback suggestion is provided, and a public transport line regulation influence index is composed of two parts of passenger flow transfer saturation and travel time extension ratio:
step 1.1-calculating the degree of saturation of passenger flow transfer
Forecasting the bus transfer demand through historical data, selecting the average value of the number of passengers getting on the bus in the early-late peak period of the pre-cancelled station in the working day of the previous week of line adjustment as the forecast value of the station passenger flow transfer demand, and using TD (time division) to forecast the bus transfer demandjRepresenting the passenger flow transfer demand for site j. Constructing a residual passenger carrying Capacity index of the substitute line in a station interval j based on a Load Factor (LF) and a rated passenger carrying Capacity (RPC) of the substitute line in a station interval with a peak, wherein the calculation method comprises the following steps:
Figure BDA0002623429170000021
in the formula:
TD-total passenger flow transfer requirement of the preset line;
ASC — remaining passenger capacity of all alternate lines;
TDj-a passenger flow transfer requirement of a pre-tuned line jth pre-revocation site;
LFijpre-adjusting the full load rate of the jth pre-revocation site interval of the ith replacement line of the line;
RPCi-pre-adjusting the nominal passenger capacity of the ith replacement line of the line;
numijpre-tuning the number of arrival shifts of the jth station of the ith replacement line in the morning and evening peak hours.
m is the total number of the alternative lines;
n is the total number of preset line sites j;
pj-early peak landing volume for pre-revoked site j.
Step 1.2-calculate the line time extension ratio
The trip time extension ratio after line adjustment is composed of three parts, namely a trip time extension ratio before line adjustment, a trip time extension ratio before line adjustment and a transfer time extension ratio.
1. Travel time extension ratio calculation before route adjustment
And selecting the total travel time as an evaluation index of travel utility, predicting the extension proportion of the total travel time of the commuter after line adjustment, and solving the travel time extension ratio as a correction coefficient of the bus route adjustment influence index.
Figure BDA0002623429170000031
In the formula:
r1-the trip time extension ratio before line conditioning;
pef-the number of commuters to the line at pre-adjusted line station e, station f;
Tefadjusting the average bus travel time from the pre-adjusted line stop e to the pre-adjusted line stop f;
Tef' pre-adjusting the average bus travel time of the line and the alternative line stations e to f after adjustment;
n-total number of pre-adjusted line sites.
2. Identification of newly added transfer sites
The identification of the newly added transfer station is considered in two categories. The first case is a partial site withdrawn type of line regulation, and the second case is a full line withdrawn type of line regulation.
3. Time-in-transit extension prediction
After the line cancels the station, partial station or full line function is undertaken by the alternative line, so the travel time of partial pedestrians can be changed. The time-in-transit may be calculated by summing the trip times for each site interval, which are estimated based on the historical data averages for the preconditioned route and the alternate route. And further, the travel time delay amount of different travel ODs after line adjustment can be calculated according to the travel time difference of the preset line and the substitute line in the same station interval.
Figure BDA0002623429170000032
In the formula
ΔTtravel-an amount of time in transit extension;
tef' adjusting the average bus travel time of the pre-adjusted route and the alternative route stops e to f but not including the transfer time.
4. Transfer time extension prediction
After the line cancels the station, the extension amount of the transfer time of the pre-adjusted line commuter is related to the average transfer time and the increment of the total transfer times. And the transfer basically occurs at the newly-added transfer points identified in the step one, and the lines before and after the transfer are known, so that the average transfer time of the transfer mode of the station can be counted based on the card swiping data. The total transfer times need to be counted and counted respectively how many commuters' travel tracks contain newly-added transfer points based on the travel OD historical data of the line commuters.
Figure BDA0002623429170000041
In the formula:
pkthe number of commuters on the line when the preset line passes through the kth newly-increased transfer point before adjustment;
tk-average bus transfer (between pre-adjusted routes or alternative routes) time for the kth new transfer point;
s is the total number of new transfer points of the pre-integer circuit.
Through the above 4 steps, the bus route-switching travel time extension ratio r can be calculated by the following formula:
Figure BDA0002623429170000042
step 1.3: bus route-switching influence index calculation
The bus route adjusting influence index is a pre-estimated index obtained by calculating after correcting based on two indexes of passenger flow transfer saturation and travel time extension ratio, wherein the former reflects the influence of a route adjusting scheme on the passenger flow supply and demand relation, and the latter reflects the travel utility change of a commuter on a pre-adjusted route, so that the rationality of an initial bus network optimizing scheme is evaluated from the perspective of a traveler. The method for calculating the bus route-switching influence index comprises the following steps:
BAI＝s·r·α (6)
in the formula, BAI is a bus route adjustment influence evaluation index, which is called a bus route adjustment influence index for short;
the alpha-scale factor is 10.
And 1.4, grading the influence of the preset line according to the bus line-regulating influence index, wherein the influence is shown in the table 1.
TABLE 1 bus diversion influence index grading
Figure BDA0002623429170000043
Figure BDA0002623429170000051
Step 2, optimizing and tracking audit based on public traffic network
After the bus network optimization scheme is implemented, the optimization effect is tracked and evaluated from the perspective of travelers, and the evaluation result can be used for improving the feedback of the evaluation and optimization method flow before adjustment. From two angles of the public traffic network and the line, the tracking audit index after the optimization of the public traffic network is calculated by combining the optimization target and fully utilizing the travel characteristic sensing data.
Step 2.1, calculating network layer optimization effect audit indexes
After the optimization scheme is implemented, in order to verify whether the optimization target is realized or not and whether the effect is improved or not in the aspects of bus travel rapidness and convenience is evaluated, the total in-transit time and the total transfer time of all travelers in a network are calculated to serve as core indexes. The calculation formula of each index is as follows:
1. average trip time
And extracting the travel time of the complete travel process of the traveler by using the travel chain data in the travel characteristic perception. Meanwhile, based on the crowd classification result in the travel characteristic perception, the average travel time is extracted and calculated according to commuting crowds and non-commuting crowds. Therefore, the influence of the line adjustment on the travel time of various types of people can be represented more finely.
The calculation formula is as follows:
Figure BDA0002623429170000052
Figure BDA0002623429170000053
in the formula:
Figure BDA0002623429170000054
-mean travel time of commuter population;
Figure BDA0002623429170000055
-average travel time of non-commuting population;
pef,co-commute passenger flow for bus stops e to f;
pef,no-non-commuting passenger flow at bus stops e to f;
tefaverage travel time for bus stops e to f.
In the optimization effect evaluation, the influence of the adjustment of the public transport network on all passengers needs to be evaluated, so that the peak time is not limited any more, and the travel time change of the passengers in all time and types is considered. However, the total time of transit and the total time of transfer in peak hours should be focused on, and the time with the most tension supply and demand relationship is taken as an optimization target.
2. Passenger average transfer coefficient
Based on mass public transportation trip chain data, average transfer coefficients of passengers are extracted, and the passengers are still divided into commuting groups and non-commuting groups in statistical dimensions. The specific calculation method is as follows:
Figure BDA0002623429170000061
Figure BDA0002623429170000062
in the formula:
Trancommuter-commuter population average transfer coefficient;
Trannon-commuter-average non-commuting population transfer factor;
numchain,co-the number of commuting trip crowd trip chains;
numtransfer,co-commuting trip population transfer times;
numJS,co-the number of commuting trip crowd travel stages;
numchain,no-the number of travel chains of non-commuting travel groups;
numtransfer,no-number of transfers of non-commuting travel populations;
numJS,no-number of travel stages of non-commuting travel population.
Step 2.2, evaluation index of optimization effect of line layer
1. Categorical impact people number extraction
Based on the IC card data, according to the transfer characteristics of travelers between the travel mode and the line after line adjustment, the influence crowd of bus line adjustment is divided into 4 types, the A bus is induced to increase the crowd, the B line is induced to increase the crowd, the C bus is lost, and the D line is lost.
TABLE 2 bus net optimization impact crowd partitioning
Figure BDA0002623429170000071
2. Frequent trip characteristic changes of passengers
And calculating the average daily travel time, travel distance, transfer time and transfer times of the evaluation object in working days in one month before and after line adjustment.
3. Calculating the travel characteristic index change
And calculating the average daily travel time, travel distance, transfer time and transfer times of one month before and after the adjustment. The rate of change of these indicators.
Step 2.3, comparing the evaluation indexes of the optimization effects of the line layer and the line layer to evaluate whether the expected optimization target is realized
When each line adjustment scheme is formulated, a decision maker often proposes a corresponding qualitative optimization target, such as 'weakening repeated lines, facilitating passenger connection' and the like, and evaluates whether the optimization scheme achieves an expected target or not by comparing the line network and the trip characteristic changes of frequent passengers before and after line adjustment aiming at the target of the specific line optimization scheme.
On the basis of an initial bus network optimization scheme, the method provides a two-stage evaluation feedback method from the perspective of travelers, and the evaluation is respectively carried out before the initial scheme is adjusted and after the bus network is optimized. The method mainly considers passenger flow transfer and passenger travel time extension which are possibly generated after line adjustment, evaluates the influence degree of an initial scheme on related travelers, and carries out correction feedback on the influence degree; the latter carries out tracking feedback on the evaluation result before adjustment and the application effect of the bus network optimization method from two levels of networks and lines according to the actual passenger flow transfer condition and the travel characteristic change of the frequent passengers in the lines after the scheme is concerned.
Compared with the prior art, the invention has the following obvious advantages and beneficial effects:
in the first stage, aiming at an initial bus network optimization scheme, passenger flow transfer and travel time extension possibly caused by line adjustment are estimated through mass bus historical travel data, a quantitative correction feedback suggestion is further provided to determine a final bus network optimization scheme, the risk that serious negative influence is possibly brought to a traveler after the bus network optimization scheme is implemented is reduced, and the feasibility of the bus network optimization scheme is improved.
And in the second stage, aiming at the effect of the bus network optimization scheme after implementation, the actual passenger flow transfer and travel characteristic change of related passengers after line adjustment are evaluated, so that the tracking evaluation of the actual effect of the bus network optimization is realized, the feedback verification of the influence estimation in the first stage is also realized, the precision of the evaluation before the scheme is continuously improved, and the flow of the bus network optimization method is gradually improved.
The evaluation feedback of the two stages is from the perspective of a traveler, the service level of the public transportation network is improved to the maximum extent, mass public transportation data which are mature day by day are fully utilized, on the basis of an initial public transportation network optimization scheme, a public transportation network optimization auditing method comprising the two stages is provided from the perspective of the traveler, the feasibility of the public transportation network optimization scheme is improved, the risk of negative influence is reduced, and meanwhile, effective means and mechanisms are provided for continuously perfecting the optimization method flow.
Drawings
FIG. 1 is a flow chart of bus network optimization audit based on traveler's perspective;
FIG. 2 is a diagram of a bus route adjustment case;
FIG. 3 is a result of grading bus lane change influence indexes;
Detailed Description
In the embodiment, an initial bus network adjustment scheme (26 days in 11 months, 26 days in 12 months and 31 days in 12 months in 2016) published by the official website of the Beijing public transportation group is taken as an example to audit the bus network optimization scheme.
And (3) taking Beijing 477 circuit adjustment as an example to evaluate the effect after circuit optimization.
Optimization objectives and methods: in order to solve the problem that the roads have no vehicles, the travel of residents is facilitated, repeated lines in a central urban area are optimized, and 477 buses are adjusted. After adjustment, the line is sent out by the Mingchunyuan station, passes through the Jingliang road, the Fengyuan road and the Fengke road to the Fengcao road, and then reaches the Guangfu carriage station along the 477 road original line, as shown in figure 2. An alternative to this line is 38 lines.
This example comprises the following steps:
step 1: calculating a public traffic route adjusting influence index based on the correction feedback evaluated before the adjustment of the line network optimization scheme;
in the calculation example, the index calculation is mainly carried out at the early peak time period, and the preset route which has great influence on the bus trip is identified.
The number of samples containing the withdrawn stations is 36, and the tuning index of 36 preset lines is calculated based on the algorithm provided by the method, and the result is shown in table 2.
TABLE 2 calculation results of bus route-changing influence index
Figure BDA0002623429170000081
Figure BDA0002623429170000091
Grading the bus route-adjusting influence indexes;
the grading result is shown in fig. 3, wherein the influence indexes of the 610 downlink bus route adjusting, 421 uplink bus route adjusting, 697 downlink bus route adjusting and 968 downlink bus route adjusting are three levels; there are also 4 lines in the second stage, the rest all at the first level.
Step 2: and tracking audit is carried out after optimization based on the public traffic network.
Based on the bus card swiping data of one month before and after line adjustment, the number of four types of population affected by bus induction, line induction, bus loss and line loss is extracted, as shown in table 3.
TABLE 3 adjustment of bus routes influencing the population
Figure BDA0002623429170000092
The transfer characteristics of frequent passengers after the line adjustment show that the total number of lost people of the line is greater than the total number of induced people, and most lost people are transferred to other bus lines.
Travel characteristic indexes of frequent passengers before and after 477-way bus adjustment are respectively extracted based on travel characteristic perception, and the result is shown in table 4.
TABLE 4 influence of bus route adjustment on travel characteristics
Figure BDA0002623429170000101
The result shows that 477 road adjustment prolongs the travel time of the frequent passengers on the bus route by about 4% on average, and the transfer times are increased by nearly 8%, which is seen to mainly have negative effects on travelers. The only descending index is the travel distance, which shows that the main factors considered when the bus enterprise adjusts the route making adjustment scheme at present are to reduce the operation mileage and the operation cost, and the benefit of the travelers is not really considered, so that the bus attraction is difficult to promote.
Compared with 477 route optimization targets published by a public transport group on the official website, the 477 route optimization targets are 'convenient residential quarter trip' and 'optimized central urban repeated route', and from the condition of passenger flow transfer, the goals are basically realized, but negative influences such as prolonged trip time, increased transfer times and the like are generated on trip people.

Claims (3)

1. A public transport network optimization auditing method based on a traveler visual angle is characterized by comprising the following steps:
step 1: based on the correction feedback evaluated before the influence of the wire mesh optimization scheme;
aiming at an initial optimization scheme, passenger flow transfer and travel time extension possibly brought by travelers are provided by estimating route adjustment through mass public transport historical travel data, a quantitative correction feedback suggestion is provided, and reference is provided for a more reasonable network optimization scheme;
step 2, tracking audit based on evaluation after optimization of the public traffic network;
after the implementation of the bus network optimization scheme, tracking and evaluating the optimization effect from the perspective of a traveler, wherein the evaluation result is used for improving the feedback of the evaluation before influence and the optimization method flow; from two angles of a public transport network and a line, an optimization target is combined, trip characteristic sensing data are fully utilized, and the adjusted line network is subjected to tracking audit by using the evaluation indexes after the public transport line network is optimized;
step 1, after an initial network optimization scheme is formed, calculating passenger flow transfer saturation and travel time extension ratio through mass multi-source public transport data to obtain a public transport line regulation influence index, and accordingly providing correction feedback based on evaluation before influence of the network optimization scheme;
if the requirements are met, carrying out the net optimization according to the original scheme, obtaining public transportation data after the net optimization implementation after the optimization scheme is implemented, entering the step 2, and respectively carrying out tracking audit based on evaluation after the net optimization from the network layer and the line layer by using the implemented multi-source public transportation data;
and (3) if the requirements are not met after the step (1) is finished, performing the site optimization, performing the audit in the step (1) again, implementing the optimization scheme after the requirements are met, and entering the step (2).
2. The bus network optimization auditing method based on traveler visual angle according to claim 1, characterized in that the implementation process of step 1 is as follows,
step 1.1-calculating the degree of saturation of passenger flow transfer
Estimating the station passenger flow transfer saturation after the line is cancelled based on the station landing amount of the pre-cancellation line and the remaining passenger carrying capacity of the replacement line, wherein the higher the saturation is, the higher the passenger flow transfer pressure is, and the lower the rationality of the adjustment scheme is; weighting and summing the saturation of all withdrawn stations based on station ascending and descending quantity ratio to serve as a basic index of a bus route adjusting influence index;
step 1.2-calculate the line time extension ratio
Travel time extension ratio calculation before route adjustment
Selecting the total travel time as an evaluation index of travel utility, estimating the extension proportion of the total travel time of the commuter after line adjustment, and solving the travel time extension ratio as a correction coefficient of the bus route adjustment influence index;
compared with the total travel time before line adjustment, the total travel time after line adjustment may increase two time costs, one is the in-transit time delay amount, and the other is the transfer time delay amount; therefore, the following steps are also required:
1) identification of newly added transfer sites
The identification of the newly added transfer station is considered in two types of situations; the first case is a line regulation type of partial site revocation; the second case is a line regulation type of full line withdrawal;
2) time-in-transit extension prediction
After the line cancels the station, partial station or full line function is undertaken by the alternative line, so the trip time of partial pedestrians can be changed; calculating the in-transit time by summing the travel time of each station interval, and estimating the travel time of each station interval based on the historical data average value of the preset line and the alternative line; calculating the travel time delay amount of different travel ODs after line adjustment according to the travel time difference of the preset line and the substitute line in the same station interval;
3) transfer time extension prediction
After the line cancels the station, the transfer time delay of the pre-adjusted line commuter is related to the average transfer time and the total transfer time increment; the transfer occurs at the newly-added transfer points identified in the step one, and the lines before and after the transfer are known, so that the average transfer time of the transfer mode of the station is counted based on the card swiping data; the total transfer times need to be counted based on travel OD historical data of line commuters, and the travel tracks of the commuters respectively containing newly-added transfer points;
step 1.3: calculating bus route-switching influence index
And 1.4, grading the bus line-adjusting influence indexes, and adjusting the line network optimization scheme again according to the grade influence and suggestions.
3. The traveler visual angle-based bus net optimization auditing method according to claim 1, characterized by comprising the step of 2.1, calculating network layer optimization effect auditing indexes
After the optimization scheme is implemented, in order to verify whether the optimization goal is achieved, the total in-transit time and the total transfer time of all travelers in a line network are selected as core indexes; the calculation formula of each index is as follows:
1) average trip time
Extracting travel time of a complete travel process of a traveler by using travel chain data in the travel characteristic perception; meanwhile, based on the crowd classification result in the travel characteristic perception, the average travel time is extracted and calculated according to commuting crowds and non-commuting crowds;
paying attention to the total time of buses in transit and the total time of transfer in peak time periods, and taking the time period with the most tense supply and demand relationship as an optimization target; in the optimization effect evaluation, the influence of the bus network adjustment on all passengers needs to be evaluated, the peak time is not limited, and the travel time change of the passengers in all time and types is considered;
2) passenger average transfer coefficient
Based on mass public transportation trip chain data, extracting an average transfer coefficient of passengers, and still dividing the passengers into commuting groups and non-commuting groups in statistical dimension;
step 2.2, evaluation index of optimization effect of line layer
1) Categorical impact people number extraction
Based on the IC card data, according to the trip mode of travelers after line adjustment and the transfer characteristics among lines, the influence crowd of bus line adjustment is divided into 4 classes, namely, A bus induced crowd, B line induced crowd, C bus lost crowd and D line lost crowd;
2) frequent trip characteristic changes of passengers
Calculating the average daily travel time, travel distance, transfer time and transfer times of the evaluation object in working days in each month before and after line adjustment;
3) calculating the travel characteristic index change
Calculating the average daily travel time, travel distance, transfer time and transfer times of one month before and after the adjustment; the rate of change of these indicators;
step 2.3, comparing the optimization effect evaluation indexes of the line layer and the line layer to evaluate whether the expected optimization target is realized;
when each line is used for making an adjustment scheme, a decision maker often proposes a corresponding qualitative optimization target, such as weakening repeated lines and facilitating passenger connection, and evaluates whether the optimization scheme achieves an expected target or not by comparing the line network and the trip characteristic changes of frequent passengers before and after line adjustment aiming at the target of the specific line optimization scheme.
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CN115222297A (en) * 2022-09-15 2022-10-21 深圳市城市交通规划设计研究中心股份有限公司 Bus route optimization adjustment scheme evaluation method, electronic device and storage medium

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CN113408833A (en) * 2021-08-19 2021-09-17 深圳市城市交通规划设计研究中心股份有限公司 Public traffic key area identification method and device and electronic equipment
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