An intelligent city public transport management method and system, and an intelligent city public transport system

Abstract

The application relates to an intelligent city bus management method and system and an intelligent city bus system. The method comprises the following steps: obtaining a passenger order submitted by a mobile terminal; generating a scheme combination based on the passenger order; calculating the expected time consumption of each candidate path in the scheme combination and pushing the candidate path and the corresponding time consumption to the mobile terminal; obtaining a preselected path submitted by the mobile terminal; dividing a time interval, obtaining the order quantity of each platform as a local path starting point in each time interval, and taking the order quantity as a characteristic value of the local path, wherein the local path is a path from the platform to a next node of each preselected path, and the node comprises a starting point, a transfer point and a terminal on the preselected path; based on the characteristic value and the order information of passengers on the vehicle, scheduling a vehicle passing through the vicinity of a station corresponding to the characteristic value and / or a vehicle passing through a local path corresponding to the characteristic value to stop or not to stop at the station corresponding to the characteristic value. The application can improve the passenger carrying efficiency of the bus system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of public transportation, in particular to an intelligent urban public transport management method and system and an intelligent urban public transport system. BACKGROUND

[0002] The current urban public transport management system is in a traditional mode. The setting of bus routes and bus stations is determined by human factors. The public transport company does not have comprehensive data on the flow of people at different times. Usually, personnel are arranged to investigate the flow of people at bus stations at regular intervals to summarize the demand for bus routes at each bus station. Based on the summary scheme, buses are dispatched at fixed intervals and travel along fixed routes. Therefore, in a certain time period, due to fluctuations in the flow of people, the public transport system cannot provide sufficient passenger carrying capacity, or cannot carry full passengers, or even causes empty running, which undoubtedly wastes a large amount of social resources and energy, and also greatly reduces the operating efficiency of the public transport company. SUMMARY

[0003] In order to improve the passenger carrying efficiency of the public transport system, the present application provides an intelligent urban public transport management method, system and intelligent urban public transport system.

[0004] In a first aspect, the present application provides an intelligent urban public transport management method, which adopts the following technical solution:

[0005] An intelligent urban public transport management method, comprising the following steps:

[0006] An information acquisition step: acquiring a passenger order submitted by a mobile terminal, wherein the passenger order includes a journey starting point and a journey ending point;

[0007] A path planning step: generating a scheme combination based on the passenger order, wherein the scheme combination includes a plurality of candidate paths, the starting point of the candidate path corresponds to the journey starting point of the passenger order, and the ending point of the candidate path corresponds to the journey ending point of the passenger order;

[0008] A path pushing step: calculating the estimated time consumption of each candidate path and pushing the candidate path and the corresponding time consumption to the mobile terminal;

[0009] A path preselection step: acquiring a preselected path submitted by the mobile terminal, wherein the preselected path is a candidate path determined by the mobile terminal based on the scheme combination;

[0010] A calculation step: dividing time intervals, obtaining the number of orders with each station as a local path starting point in each time interval, and taking the number of orders as a characteristic value of the local path, wherein the local path is a path from the station to the next node of each preselected path, and the node includes the starting point, the transfer point and the ending point on the preselected path;

[0011] Vehicle dispatching step: based on the characteristic value and the order information of the passengers on the vehicle, dispatch the vehicle passing the station near the characteristic value and / or the vehicle passing the local path corresponding to the characteristic value to stop or not to stop at the station corresponding to the characteristic value.

[0012] By adopting the above technical scheme, the passenger inputs the trip starting point and the trip ending point to the mobile terminal in advance, the system optimizes several different selected paths for the passenger to select based on the passenger order in the information acquisition step and the path planning step. After calculating the expected time consumption of the selected path, the system pushes it to the passenger for selection. After the passenger selects the corresponding selected path, the system calculates the number of passengers at each station to determine the demand degree of the bus corresponding to each route at each station. Then the system adjusts the pre-selected path based on the overall selection of the passengers, so that the bus can skip the station with no or few passengers, or temporarily stop at the station with many passengers, thereby improving the passenger carrying capacity of the vehicle and improving the passenger carrying efficiency.

[0013] Optionally, the vehicle dispatching step comprises the following steps:

[0014] Based on the calculation strategy, the average expected waiting time and the total expected waiting time corresponding to each characteristic value are obtained, wherein the expected waiting time is the difference between the initial boarding time and the order submission time of the passenger, or the difference between the transfer boarding time and the transfer alighting time of the passenger;

[0015] Judge the relative size of the total expected waiting time corresponding to the characteristic value and the upper and lower limits of the first threshold range, and the relative size of the average expected waiting time and the upper and lower limits of the second threshold range, and based on the judgment result and the dispatching strategy, dispatch the vehicle passing the station near the characteristic value and / or the vehicle passing the local path corresponding to the characteristic value to stop or not to stop at the station corresponding to the characteristic value, wherein the first threshold range and the second threshold range are dynamically adjusted based on the current time period.

[0016] Optionally, the calculation strategy comprises the following steps:

[0017] Step one: select a pre-selected path corresponding to a characteristic value, obtain the remaining passenger capacity of each vehicle in front of the station on the pre-selected path, and calculate the net remaining passenger capacity r of each vehicle based on the passenger order and the remaining passenger capacity i , wherein the net remaining passenger capacity r i = remaining passenger capacity - the number of passengers boarding at each node between the vehicle and the station + the number of passengers alighting at each node between the vehicle and the station;

[0018] Step two: based on the order from near to far of the vehicle to the station, the net remaining passenger capacity of each vehicle in front of the station is sequentially added until the cumulative value is greater than the characteristic value.

[0019] Step three: obtaining the required travel time t of the vehicle corresponding to the net residual passenger capacity accumulated in step two to the station corresponding to the characteristic value i , calculating the total expected waiting time wherein n is the number of vehicles corresponding to the net residual passenger capacity accumulated in step two, and i is mapped from small to large to each vehicle from near to far before the station corresponding to the characteristic value on the pre-selected path,

[0020] Step four: calculating the average expected waiting time = total expected waiting time / characteristic value.

[0021] By using the above technical solution, the passenger quantity of the current platform and the urgency of the bus capacity are evaluated by the average expected waiting time and the total expected waiting time, so that the vehicle is deployed accordingly.

[0022] Optionally, the scheduling strategy includes the following steps:

[0023] If the average expected waiting time > upper limit of the first threshold range, and the total expected waiting time > upper limit of the second threshold range, the departure frequency of the pre-selected path corresponding to the local path is increased, or the pre-selected path is adjusted based on the passenger orders corresponding to each pre-selected path passing through the local path, wherein the method of adjusting the pre-selected path includes controlling the vehicles not passing through the pre-selected path to pass through the station, and / or the vehicles not planned to stop at the station to stop at the station, and / or scheduling the vehicles to transfer at other stations to transfer at the station based on the transfer strategy;

[0024] If the average expected waiting time < lower limit of the first threshold range, and the total expected waiting time < lower limit of the second threshold range, the departure frequency of the pre-selected path corresponding to the local path is reduced, or the pre-selected path is adjusted based on the passenger orders corresponding to each pre-selected path passing through the local path, wherein the method of adjusting the pre-selected path includes controlling the vehicles passing through the pre-selected path to bypass the station, and / or the vehicles stopping at the station not to stop at the station, and / or scheduling the vehicles to transfer at the station to transfer at other stations based on the transfer strategy;

[0025] If the average expected waiting time < upper limit of the first threshold range, and the total expected waiting time > upper limit of the second threshold range, no scheduling is performed.

[0026] If the average expected waiting time > upper limit of the first threshold range, and the total expected waiting time < lower limit of the second threshold range, no scheduling is performed.

[0027] By adopting the technical scheme, when the average predicted waiting time is greater than the upper limit of the first threshold range and the total predicted waiting time is greater than the upper limit of the second threshold range, corresponding to the case that there are many people in the station and the buses on the road are sparse, the buses can be added, the flow can be divided, or the passengers originally intended to transfer at the station can be modified to transfer at other stations, so that the transport capacity for the passengers at the station is improved and the waiting time of the passengers is reduced.

[0028] When the average predicted waiting time is less than the lower limit of the first threshold range and the total predicted waiting time is less than the lower limit of the second threshold range, corresponding to the case that there are few people in the station and the buses on the road are dense, the buses can be reduced, the transport can be concentrated, or the passengers at other stations with tight transport capacity can be allocated to transfer at the station, so that the waste of transport capacity is reduced.

[0029] Optionally, the transfer strategy comprises the following steps:

[0030] The standby path corresponding to the station of the characteristic value is obtained, wherein the standby path and the preselected path each comprise a front section, a middle section and a rear section, the front section and the rear section of the standby path and the preselected path overlap, the station corresponding to the characteristic value is located in the middle section of the preselected path, and the middle sections of the standby path and the preselected path are separated from each other;

[0031] If the average predicted waiting time is greater than the upper limit of the first threshold range, the total predicted waiting time is greater than the upper limit of the second threshold range, and the transfer points of the passengers on the buses on the standby path all do not belong to the middle section of the standby path, the driving path of the buses on the standby path is switched to the preselected path and the buses stop at the station corresponding to the characteristic value;

[0032] If the average predicted waiting time is less than the lower limit of the first threshold range, the total predicted waiting time is less than the lower limit of the second threshold range, and the transfer points of the passengers on the buses on the preselected path all do not belong to the middle section of the preselected path, the driving path of the buses on the preselected path is switched to the standby path.

[0033] By adopting the technical scheme, the middle section is the branch of the standby path and the preselected path, when the passengers on the buses on the two routes do not need to get off at the middle section, the buses can be exchanged as needed, and the buses on the route with low demand for transport capacity can be allocated to the route with high demand for transport capacity.

[0034] Optionally, in the driving process of the buses, whether a passenger gets off at the next node is judged based on the passenger order of the passenger on the bus, if not, whether a passenger needs to get on the bus at the next node is judged, and if not, the buses do not stop at the station.

[0035] In the current public transport system, whether there are people at the station or not, the driver needs to control the buses to stop, open the door and start, by adopting the technical scheme, whether there is a demand for getting on and off the bus can be directly known through the passenger order, so that the operation efficiency of the buses is improved.

[0036] Optionally, the path pre-selection step and the calculation step further comprise an assignment step, which comprises the following steps:

[0037] The buses are divided into near-range buses and far-range buses, and the near-range buses and the far-range buses are alternately dispatched;

[0038] The passenger orders with the starting points at the station corresponding to the characteristic value are divided into near-range orders and far-range orders, wherein the near-range orders are the orders with the number of nodes between the transfer points / destinations and the station being less than a preset threshold, and the far-range orders are the orders with the number of nodes between the transfer points / destinations and the station being greater than the preset threshold; the preset threshold has multiple values and corresponds to each station respectively;

[0039] It is judged whether the remaining passenger capacity of the far-range bus to be arrived at a station is greater than the number of far-range orders, if yes, the near-range bus is controlled to receive only the passengers corresponding to the near-range orders, and if no, the near-range bus is arranged to receive the passengers of the near-range orders and the far-range orders;

[0040] It is judged whether the remaining passenger capacity of the near-range bus to be arrived at a station is greater than the number of near-range orders, if yes, the far-range bus is controlled to receive only the passengers corresponding to the far-range orders, and if no, the far-range bus is arranged to receive the passengers of the near-range orders and the far-range orders.

[0041] By adopting the above technical scheme, the preset threshold corresponds to the starting points and the transfer points of the passengers at each station, and the near-range buses can receive the passengers with the transfer points or the destinations being close to the current station as much as possible, and the far-range buses can receive the passengers with the transfer points or the destinations being far away from the current station as much as possible. If the passengers randomly get on the buses, the destinations and the transfer points of the passenger orders are likely to be uniformly distributed at each node, and thus the buses need to frequently stop for picking up or dropping off passengers. When the number of passengers at the station is moderate, the near-range buses and the far-range buses can receive the passengers at the station respectively, the near-range buses first take the near-range passengers away, and then the far-range buses take the far-range passengers away. Therefore, compared with the prior art, the probability of the near-range buses and the far-range buses stopping for picking up passengers is reduced or even halved, and the probability of the near-range buses stopping for dropping off passengers in the near-range and the probability of the far-range buses stopping for dropping off passengers in the far-range are also reduced, so that the running efficiency of the buses is greatly improved.

[0042] In a second aspect, the present application provides an intelligent urban bus management system, which adopts the following technical scheme:

[0043] An intelligent urban bus management system, comprising:

[0044] A data information collection module is configured to acquire passenger orders submitted by a mobile terminal, wherein the passenger orders comprise a starting point and a destination.

[0045] a path planning module configured to obtain a passenger order and generate a scheme combination, wherein the scheme combination comprises a plurality of candidate paths, a starting point of the candidate path corresponds to a starting point of a journey of the passenger order, and an ending point of the candidate path corresponds to an ending point of the journey of the passenger order;

[0046] a path pushing module configured to calculate an estimated time consumption of each candidate path and push the candidate path and the corresponding time consumption to a mobile terminal;

[0047] a path preselection module configured to obtain a preselected path submitted by the mobile terminal, wherein the preselected path is a candidate path determined by the mobile terminal based on the scheme combination;

[0048] a calculation module configured to divide time intervals, obtain a number of orders with each station as a starting point of a local path in each time interval, and take the number of orders as a characteristic value of the local path, wherein the local path is a path from the station to a next node of each preselected path, and the node includes a starting point, a transfer point and an ending point on the preselected path;

[0049] a vehicle scheduling module configured to schedule a vehicle passing through a station near the characteristic value and / or a vehicle passing through a local path of the characteristic value to stop or not to stop at the station corresponding to the characteristic value according to the characteristic value and order information of passengers on the vehicle.

[0050] By adopting the above technical solution, the passenger inputs a starting point and an ending point of a journey to the mobile terminal in advance, the system plans paths based on the passenger order in the information obtaining step and the path planning step, and optimizes several selectable different candidate paths for the passenger to select. After the estimated time consumption of the candidate path is calculated, the candidate path is pushed to the passenger for selection. After the passenger selects a corresponding candidate path, the system calculates the number of passengers at each station, thereby determining the demand degree of the bus of each route corresponding to each station. Then, the system adjusts the preselected path based on the overall selection of the passengers, so that the bus can skip the station with no or few passengers or temporarily stop at the station with many passengers, thereby improving the passenger carrying capacity of the vehicle and the passenger carrying efficiency.

[0051] In a third aspect, the present application provides an intelligent urban public transport system, which adopts the following technical solution:

[0052] An intelligent urban public transport system comprises a mobile terminal, a server located locally or in the cloud, a bus, a bus station and a connecting vehicle, the connecting vehicle is used to connect passengers from a departure place to the bus station and to connect passengers from a drop-off station to a final destination.

[0053] Optionally, the mobile terminal is provided with a data information collection module and an order payment settlement module, the data information collection module is configured to obtain a passenger order submitted by the mobile terminal, and the order payment settlement module is configured to generate a corresponding journey cost after the order ends.

[0054] The server comprises at least a data information storage module and a data analysis calculation module, the data information storage module is used to acquire the passenger order uploaded by the data information collection module, and determine the final order and record storage after the trip ends; the data analysis calculation module comprises:

[0055] A line planning adjustment unit is configured to dispatch vehicles passing through stations near the characteristic value and / or vehicles passing through local paths corresponding to the characteristic value to stop or not to stop at the stations corresponding to the characteristic value according to the characteristic value and the order information of passengers on the vehicles; a vehicle dispatch adjustment unit is configured to adjust the departure frequency of the vehicles according to the characteristic value and the order information of passengers on the vehicles.

[0056] A connecting vehicle dispatch unit is configured to adjust the number of connecting vehicles on the stations corresponding to the characteristic value according to the characteristic value and the order information of passengers on the vehicles.

[0057] In a fourth aspect, the application provides a host, which adopts the following technical scheme:

[0058] A host comprises a memory and a processor, and the memory stores a computer program capable of being loaded and executed by the processor to perform the above method.

[0059] In a fifth aspect, the application provides a readable storage medium, which adopts the following technical scheme:

[0060] A readable storage medium stores a computer program capable of being loaded and executed by the processor to perform the above method. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 is a flowchart of an intelligent urban public transport management method in the embodiments of the application.

[0062] Figure 2 is a module connection diagram of an intelligent urban public transport system in the embodiments of the application.

[0063] Figure 3 is a schematic diagram for introducing a passenger travel process in the embodiments of the application. DETAILED DESCRIPTION

[0064] The above description is only a specific implementation of the application. Figure 1 and Figure 2 The application will be further described in detail.

[0065] The current urban public transport management system is in a traditional mode. The setting of bus routes and bus stops is arranged by human factors. The bus departs at fixed intervals and travels along fixed routes. Passengers go to the station and choose the bus according to their own judgment. Many times, passengers need to use map navigation software to find the optimal route and need to keep an eye on the passing bus to avoid missing the bus. In many cases, such as rush hour, the rapid growth of passenger flow can easily lead to the inability of the bus system to provide sufficient passenger carrying capacity. The working hours of working days are prone to be unable to carry full passengers or even to occur empty running. This is due to the inability to instantly and accurately understand the passenger demand at each station, which will waste a large amount of social resources and energy, and the operating efficiency of the bus company will also be greatly reduced.

[0066] Based on this, the embodiment proposes an intelligent bus station. The bus station can be an ordinary station or a semi-closed space set on the roadside or on the road and accessed through a gate, similar to the station of the BTR rapid bus system. Passengers use a dedicated APP or a small program or a third-party APP or a small program or scan the two-dimensional code bound to the unique code information of the station on the station. The system generates a two-dimensional code valid for a limited time for passengers to scan the two-dimensional code through the gate, or for the bus to identify when boarding. The bus can control the length of time the bus stops according to the number of passengers boarding the bus, and passengers do not need to crowd at the door to buy tickets during the boarding process, so the working efficiency of the bus is high. Each bus does not travel along the same route, so it avoids the problem of passengers missing tickets or evading tickets, that is, buying low-priced tickets for nearby stations and taking the bus to distant stations, or not buying tickets when boarding. Therefore, the bus company can also price tickets according to distance, avoiding the need for manual ticketing and supervision of passengers getting off.

[0067] Optionally, in order to adapt to the intelligent bus station, the embodiment of the application discloses an intelligent urban bus management method for further improving the passenger carrying efficiency of the bus system. Of course, the intelligent bus station does not limit the independent implementation of the method, but any other bus station or bus system or other device that can use the method to improve the passenger carrying efficiency can use the method. Similarly, the bus management method is not limited to buses, but can also be applied to public taxis and other public transportation methods.

[0068] Reference Figure 1 The intelligent urban bus management method includes the following steps S1-S6, wherein the serial number of the steps does not limit the order of the steps, and the steps can be added, deleted or adjusted in sequence according to actual use.

[0069] S1. Information acquisition step: acquiring a passenger order submitted by a mobile terminal, wherein the passenger order includes a journey starting point and a journey ending point.

[0070] The mobile terminal can be a mobile device such as a mobile phone, a tablet computer, a smart watch, etc., or a fixed networking device such as a PC or a smart ticketing machine at a bus stop, etc. Any device that has an application program installed for collecting passenger orders can be used. The passenger order includes various information, at least including the starting point and the ending point of the passenger's trip, and optionally, a unique identification code for binding the passenger's identity.

[0071] S2. Path planning step: generating a scheme combination based on the passenger order, wherein the scheme combination includes multiple candidate paths, the starting point of the candidate path corresponds to the starting point of the passenger order, and the ending point of the candidate path corresponds to the ending point of the passenger order.

[0072] The system has pre-recorded planned paths, and the approximate motion trajectories of these paths are limited within a certain range, and different paths can intersect, thereby generating transfer points, at which the passenger can switch to other paths. Based on the current road conditions, the system pre-recorded map, the current bus position, and big data analysis processing, the system can give several optimal candidate paths according to the distance, time consumption, and cost. For example, the road conditions in different regions vary greatly at different time periods, and the congestion of the road has a transitivity with the passage of time. Therefore, before a sufficient number of buses arrive at a certain location, the candidate path can avoid the road sections that are not congested at present but will be congested soon, so as to avoid congestion when passing through and aggravate the congestion. For another example, some short candidate paths will have a longer time consumption due to congestion. Some long candidate paths will have a shorter time consumption due to smoothness. The cost is related to the time consumption and the distance, so the distance, time consumption, and cost can be used as three different weight factors.

[0073] S3. Path pushing step: calculating the predicted time consumption of each candidate path and pushing the candidate path and the corresponding time consumption to the mobile terminal.

[0074] S4. Path pre-selection step: obtaining the pre-selected path submitted by the mobile terminal, wherein the pre-selected path is a candidate path determined by the mobile terminal based on the scheme combination.

[0075] After the system processes the data on the server or in the cloud and obtains the scheme combination, the time consumption, cost, and distance of the candidate paths are calculated and pushed to the mobile terminal for the passenger to select on the mobile terminal. After the passenger selects, the mobile terminal uploads the data to the server or the cloud, and the server or the cloud performs the next calculation based on the data.

[0076] S5. Computing step: dividing time intervals, obtaining the number of orders of each station as the starting point of the local path in each time interval, and taking the number of orders as the characteristic value of the local path, wherein the local path is the path from the station to the next node of each pre-selected path, and the node includes the starting point, transfer point and terminal point on the pre-selected path.

[0077] Because the road condition changes quickly and the number of passenger orders has discrete properties, it is necessary to divide time intervals to reduce the data calculation pressure. At the same time, dividing the newly added passenger orders by time interval can uniformly process the passenger orders and schedule the vehicles.

[0078] S6. Vehicle scheduling step: based on the characteristic value and the order information of the passengers on the vehicle, scheduling the vehicles passing through the station near the characteristic value and / or the vehicles passing through the local path corresponding to the characteristic value to stop or not to stop at the station corresponding to the characteristic value.

[0079] Because the number of passenger orders at each station is different, and the number of buses on each path is different, it is necessary to schedule the buses to stop at stations with more passengers to receive passengers, or to place passengers who need to transfer at nearby stations with fewer passengers, or to change the route to nearby stations with more passengers to receive passengers, or not to stop at stations with fewer passengers to improve the running speed, but any scheduling method that can improve the efficiency of vehicle passenger carrying is acceptable.

[0080] Optionally, the vehicle scheduling step includes the following steps:

[0081] S61. Obtaining the average expected waiting time and the total expected waiting time corresponding to each characteristic value based on the calculation strategy, wherein the expected waiting time is the difference between the passenger's expected initial boarding time and the order submission time, or the difference between the passenger's transfer boarding time and the transfer alighting time.

[0082] S62. Judging the relative size of the total expected waiting time corresponding to the characteristic value and the upper and lower limits of the first threshold range, and the relative size of the average expected waiting time and the upper and lower limits of the second threshold range, and scheduling the vehicles passing through the station near the characteristic value and / or the vehicles passing through the local path corresponding to the characteristic value to stop or not to stop at the station corresponding to the characteristic value based on the judgment result and the scheduling strategy, wherein the first threshold range and the second threshold range are dynamically adjusted based on the current time period.

[0083] This vehicle scheduling step evaluates the number of passengers at the current station and the urgency of bus capacity through the average expected waiting time and the total expected waiting time, so as to make corresponding deployment of the vehicles. The first threshold range and the second threshold range are related to the current time period, for example, the second threshold range can be defined as 5-10 minutes during the peak hours, and can be defined as 4-7 minutes during the working hours on weekdays.

[0084] wherein the calculation strategy comprises the following steps:

[0085] S601. Select a pre-selected path corresponding to a characteristic value, obtain the remaining passenger capacity of each vehicle before the station on the pre-selected path, and calculate the net remaining passenger capacity r of each vehicle based on the passenger order and the remaining passenger capacity i wherein the net remaining passenger capacity r i = the remaining passenger capacity - the number of passengers getting on at each node between the vehicle and the station + the number of passengers getting off at each node between the vehicle and the station;

[0086] S602. Based on the order of the vehicles approaching the station, the net remaining passenger capacity of each vehicle before the station is sequentially added until the cumulative value is greater than the characteristic value.

[0087] S603. Obtain the required driving time t of the vehicle corresponding to the added net remaining passenger capacity to the station corresponding to the characteristic value i , calculate the total predicted waiting time wherein n is the number of vehicles corresponding to the added net remaining passenger capacity in step 2, and i is sequentially mapped to each vehicle from near to far before the station corresponding to the characteristic value on the pre-selected path from small to large,

[0088]

[0089] S604. Calculate the average predicted waiting time = total predicted waiting time / characteristic value.

[0090] For example, a pre-selected path has a first station, a second station and a third station in turn according to the driving order of the bus, and the third station is the station corresponding to the characteristic value. The number of passenger orders corresponding to the pre-selected path at the first station in the time interval is 10, the number of passenger orders corresponding to the pre-selected path at the third station in the time interval is 15, and the number of passenger orders corresponding to the pre-selected path at the third station in the time interval is 20, then the characteristic value is 20. There is one bus before the first station, between the first station and the second station, and between the second station and the third station, respectively, which are A bus, B bus and C bus, and the corresponding net remaining passenger capacity is 8, 10 and 9 respectively. The distance between each bus and the next station is 2 minutes, the distance between the first station and the second station is 5 minutes, and the distance between the second station and the third station is 7 minutes. The number of vehicles corresponding to the added net remaining passenger capacity is 3, the total waiting time S = 9x2 + 10x(2+7) + (20-9-10)x(2+5+7) = 122, and the average waiting time = 122 / 20 = 6.1.

[0091] According to the average expected waiting time and the total expected waiting time, the number of passengers at the current platform and the urgency of the bus capacity can be evaluated, and the vehicle can be deployed accordingly.

[0092] Optionally, the scheduling strategy includes the following judgments, and corresponding steps are executed according to different judgments:

[0093] Judgment one: if the average expected waiting time is greater than the upper limit of the first threshold range, and the total expected waiting time is greater than the upper limit of the second threshold range, the departure frequency of the pre-selected path corresponding to the local path is increased, or the pre-selected path is adjusted based on the passenger order corresponding to each pre-selected path passing through the local path, wherein the method of adjusting the pre-selected path includes controlling the vehicle not passing through the pre-selected path to pass through the station, and / or the vehicle not planned to stop at the station to stop at the station, and / or scheduling the vehicle to transfer at other stations to the station based on the transfer strategy;

[0094] Judgment two: if the average expected waiting time is less than the lower limit of the first threshold range, and the total expected waiting time is less than the lower limit of the second threshold range, the departure frequency of the pre-selected path corresponding to the local path is reduced, or the pre-selected path is adjusted based on the passenger order corresponding to each pre-selected path passing through the local path, wherein the method of adjusting the pre-selected path includes controlling the vehicle passing through the pre-selected path to bypass the station, and / or the vehicle stopping at the station to not stop at the station, and / or scheduling the vehicle to transfer at the station to other stations based on the transfer strategy;

[0095] Judgment three: if the average expected waiting time is less than the upper limit of the first threshold range, and the total expected waiting time is greater than the upper limit of the second threshold range, no scheduling is performed;

[0096] Judgment four: if the average expected waiting time is greater than the upper limit of the first threshold range, and the total expected waiting time is less than the lower limit of the second threshold range, no scheduling is performed.

[0097] When the average expected waiting time is greater than the upper limit of the first threshold range, and the total expected waiting time is greater than the upper limit of the second threshold range, it corresponds to the case that there are many people in the station and the vehicles on the road are sparse, at this time, the vehicle can be selected to increase, shunt, or the passengers originally intended to transfer to other stations can be modified to transfer at the station, thereby improving the capacity of the passengers at the station and reducing the waiting time of the passengers.

[0098] When the average expected waiting time is less than the lower limit of the first threshold range, and the total expected waiting time is less than the lower limit of the second threshold range, it corresponds to the case that there are few people in the station and the vehicles on the road are dense, at this time, the vehicle can be reduced, concentrated transportation, or the passengers of other stations with tight capacity can be allocated to the station for transfer, thereby reducing the waste of capacity.

[0099] The transfer strategy includes the following steps:

[0100] acquire a standby path corresponding to the station of the characteristic value, wherein the standby path and the preselected path each comprise a front section, a middle section and a rear section, the front section and the rear section of the standby path and the preselected path overlap, the station corresponding to the characteristic value is located in the middle section of the preselected path, and the middle sections of the standby path and the preselected path are separated from each other;

[0101] if the average predicted waiting time is greater than the upper limit of the first threshold range, the total predicted waiting time is greater than the upper limit of the second threshold range, and the transfer points of the passengers on the vehicle on the standby path are all not located in the middle section of the standby path, then the driving path of the vehicle on the standby path is switched to the preselected path and the vehicle is stopped at the station corresponding to the characteristic value;

[0102] if the average predicted waiting time is less than the lower limit of the first threshold range, the total predicted waiting time is less than the lower limit of the second threshold range, and the transfer points of the passengers on the vehicle on the preselected path are all not located in the middle section of the preselected path, then the driving path of the vehicle on the preselected path is switched to the standby path.

[0103] For example, for each route, there are several standby routes associated with the route, and the standby routes can bypass the stop on the path. By evaluating the average predicted waiting time, the total predicted waiting time, the number of passengers on the vehicle, and the passenger's alighting point on the standby routes, the vehicle can be dispatched on different routes.

[0104] Optionally, the system can further perform the following steps: during the driving of the vehicle, it is judged whether there is a passenger getting off at the next node based on the passenger order of the passenger on the vehicle, if not, it is judged whether there is a passenger getting on at the next node, if not, the vehicle is not stopped at the station.

[0105] In the current real-time bus scheme, the driver needs to control the bus to stop, open the door and start, regardless of whether there is a passenger on the station, which not only affects the average driving speed of the vehicle, but also increases the invalid work of the driver. Through this step, it can be directly known from the passenger order whether there is a demand for getting on and off, thereby improving the operation efficiency of the vehicle.

[0106] Optionally, between the path preselection step and the calculation step, there can further be an allocation step, which comprises the following steps:

[0107] Step 1: divide the bus into short-range buses and long-range buses, and alternately send out the short-range buses and the long-range buses; Step 2: divide the passenger orders with the boarding starting point located at the station corresponding to the characteristic value into short-range orders and long-range orders, wherein the short-range orders are orders with the number of nodes between the transfer point / terminal and the station less than a preset threshold, and the long-range orders are orders with the number of nodes between the transfer point / terminal and the station greater than the preset threshold; the preset threshold has multiple values and corresponds to each station respectively;

[0108] determining whether the remaining passenger capacity of the approaching bus is greater than the number of the near-order, if yes, controlling the bus to receive only the passengers corresponding to the near-order; if no, arranging the bus to receive the passengers of the near-order and the remote-order;

[0109] determining whether the remaining passenger capacity of the approaching bus is greater than the number of the near-order, if yes, controlling the bus to receive only the passengers corresponding to the near-order; if no, arranging the bus to receive the passengers of the near-order and the remote-order.

[0110] If the number of passengers at the current station is large, the near-bus and the remote-bus can also be classified so that the near-bus and the remote-bus receive the passengers getting off at the same station, and the number of stops of the near-bus and the remote-bus is reduced, thus the deceleration and acceleration process of the bus is greatly reduced, the overall operation efficiency is greatly improved, and thus the increased waiting time of the passengers is compensated by the reduced running time of the bus. Since the passengers can submit the passenger order in advance on the mobile terminal and can also reserve the getting-on time, the increased waiting time of the passengers is actually not theoretically greatly increased, and even can be reduced as the passengers are familiar with the order submission mechanism. Therefore, through the scheme, the number of trips of a bus per day is increased, and the arrival speed of the passengers is also improved.

[0111] The application also discloses an intelligent urban bus management system, comprising:

[0112] a data information collection module configured to acquire a passenger order submitted by a mobile terminal, wherein the passenger order comprises a travel starting point and a travel ending point;

[0113] a path planning module configured to acquire the passenger order and generate a scheme combination, wherein the scheme combination comprises a plurality of to-be-selected paths, a starting point of the to-be-selected path corresponds to the travel starting point of the passenger order, and an ending point of the to-be-selected path corresponds to the travel ending point of the passenger order;

[0114] a path pushing module configured to calculate an estimated time consumption of each to-be-selected path and push the to-be-selected path and the corresponding time consumption to the mobile terminal;

[0115] a path pre-selection module configured to acquire a pre-selected path submitted by the mobile terminal, wherein the pre-selected path is a to-be-selected path determined by the mobile terminal based on the scheme combination;

[0116] a calculation module configured to divide time intervals, obtain the number of orders with each station as a local path starting point in each time interval, and take the number of orders as a characteristic value of the local path, wherein the local path is a path from the station to a next node of each pre-selected path, and the node comprises a starting point, a transfer point and an ending point on the pre-selected path;

[0117] The vehicle scheduling module is configured to dispatch vehicles passing by the station corresponding to the characteristic value and / or vehicles passing by the local path corresponding to the characteristic value to stop or not to stop at the station corresponding to the characteristic value according to the characteristic value and the order information of passengers on the vehicles.

[0118] The application also discloses an intelligent city public transport system, which refers to Figure 2 The intelligent city public transport system comprises a mobile terminal, a server located on a local or cloud side, a bus, a bus station and a connecting vehicle.

[0119] The mobile terminal is provided with a data information collection module and an order payment settlement module.

[0120] The server is provided with at least a data information storage module and a data analysis calculation module.

[0121] The line planning adjustment unit is configured to dispatch vehicles passing by the station corresponding to the characteristic value and / or vehicles passing by the local path corresponding to the characteristic value to stop or not to stop at the station corresponding to the characteristic value according to the characteristic value and the order information of passengers on the vehicles.

[0122] The connecting vehicle scheduling unit is configured to adjust the number of connecting vehicles at the station corresponding to the characteristic value according to the characteristic value and the order information of passengers on the vehicles.

[0123] The mobile terminal can be a mobile device such as a mobile phone, a tablet computer or a smart watch, or a fixed network device such as a PC or a bus station intelligent ticketing machine.

[0124] For example, the information storage module is an information storage device of the device in the cloud server, and all passenger order data information collected by the information data collection module is transmitted through the Internet and stored in the information storage module; the information analysis module is a functional module of the cloud server control center, responsible for passenger demand collected by the information data collection module and stored in the information storage module, and based on the current passenger quantity of each route and passenger order calculation and analysis, one or more vehicle route planning is obtained. The information analysis module can also give vehicle driving route adjustment scheme in different time periods and departure frequency and interval time scheme in different time periods according to the big data operation of the departure platform and the destination platform of the passengers in different time periods. The payment settlement module provides multiple payment support for the formation of the cost after the passenger selects the travel route, such as using UnionPay card, Alipay or WeChat payment and the like, and the completed payment order is transmitted to the information storage module.

[0125] Referring to Figure 3 The connecting vehicle stops at the closed platform of the bus station, and the connecting vehicle can be an electric vehicle, an oil-powered vehicle or a hybrid vehicle according to the power source, and can be an unmanned vehicle or a manned vehicle according to the driving mode. The passenger publishes a passenger order through a mobile terminal at the departure place, and the data analysis and calculation module receives the passenger order, and the connecting vehicle scheduling unit arranges the connecting vehicle to depart from the station to pick up the passenger, until the passenger is connected to the bus station. The route planning adjustment unit and the vehicle scheduling adjustment unit plan the bus route for the customer based on the passenger order information and the current road conditions, and the passenger takes the bus along the planned route from the boarding station to the alighting station, and can transfer at several transfer stations in between. After the passenger arrives at the alighting station, the connecting vehicle scheduling unit arranges the connecting vehicle at the bus station to connect the passenger to the final destination of the passenger.

[0126] The application also discloses a host computer comprising a memory and a processor, wherein the memory stores a computer program capable of being loaded and executed by the processor to perform the intelligent urban bus management method.

[0127] The application also discloses a readable storage medium storing a computer program capable of being loaded and executed by the processor to perform the intelligent urban bus management method.

[0128] The above are preferred embodiments of the application, not to limit the protection scope of the application, therefore: all equivalent changes made according to the structure, shape, principle of the application shall be covered within the protection scope of the application.

Claims

1. A smart urban public transport management method, characterized in that, Includes the following steps: Information acquisition steps: Obtain passenger orders submitted via mobile device, where passenger orders include the origin and destination of the trip; Route planning steps: Generate a combination of routes based on passenger orders. The combination of routes includes multiple candidate routes. The starting point of the candidate route corresponds to the starting point of the passenger order's journey, and the ending point of the candidate route corresponds to the ending point of the passenger order's journey. Path push steps: Calculate the estimated time for each candidate path and push the candidate path and corresponding time to the mobile device; Path pre-selection step: Obtain the pre-selected path submitted by the mobile device, where the pre-selected path is a candidate path determined by the mobile device based on the combination of solutions; Calculation steps: Divide the time interval, obtain the number of orders at each station as the starting point of the local path in each time interval, and use the number of orders as the feature value of the local path. The local path is the path from the station to the next node for each pre-selected path. The node includes the starting point, transfer point and the ending point on the pre-selected path. Vehicle dispatching steps: Based on the feature value and the order information of the passengers on board, dispatch vehicles that pass near the station corresponding to the feature value and / or vehicles that pass on the local path corresponding to the feature value to stop or not stop at the station corresponding to the feature value. The vehicle dispatching process includes the following steps: The average and total estimated waiting time for each feature value are obtained based on the calculation strategy. The estimated waiting time is the difference between the passenger's estimated initial boarding time and the time when the order is submitted, or the difference between the passenger's boarding time and alighting time when transferring. The relative size of the total estimated waiting time corresponding to the feature value with the upper and lower limits of the first threshold range, and the relative size of the average estimated waiting time with the upper and lower limits of the second threshold range are determined. Based on the determination results and scheduling strategies, vehicles passing near the station corresponding to the feature value and / or vehicles passing on the local path corresponding to the feature value are scheduled to stop at or not stop at the station corresponding to the feature value. The first threshold range and the second threshold range are dynamically adjusted based on the current time period. The computational strategy includes the following steps: Step 1: Select a pre-selected route corresponding to a feature value, obtain the remaining passenger capacity of each vehicle currently located before that station on the pre-selected route, and calculate the net remaining passenger capacity r of each vehicle based on passenger orders and remaining passenger capacity. i Wherein, net passenger capacity = remaining passenger capacity - number of passengers boarding at each node between the vehicle and the station + number of passengers alighting at each node between the vehicle and the station; Step 2: Based on the order of vehicles arriving at the station from near to far, the net remaining passenger capacity of each vehicle before the station is accumulated sequentially until the accumulated value is greater than the characteristic value. Step 3: Obtain the required travel time t of the vehicle corresponding to the net surplus passenger capacity accumulated in Step 2 to the station corresponding to the characteristic value. i Calculate the total estimated waiting time Where n is the number of vehicles corresponding to the net passenger capacity accumulated in step two, and i is mapped sequentially from smallest to largest to the vehicles on the pre-selected route before the station corresponding to the feature value, from nearest to farthest. Step 4: Calculate the average estimated waiting time = total estimated waiting time / characteristic value; The scheduling strategy includes the following steps: If the average estimated waiting time is greater than the upper limit of the first threshold range and the total estimated waiting time is greater than the upper limit of the second threshold range, then the departure frequency of the pre-selected route corresponding to the local route will be increased, or the pre-selected route will be adjusted based on passenger orders corresponding to each pre-selected route that passes through the local route. The method of adjusting the pre-selected route includes controlling vehicles that do not pass through the pre-selected route to go through the station, and / or allowing vehicles that are not planned to stop at the station to stop at the station, and / or scheduling vehicles that transfer at the station to transfer at other stations based on the transfer strategy. If the average estimated waiting time is less than the lower limit of the first threshold range and the total estimated waiting time is less than the lower limit of the second threshold range, then the departure frequency of the pre-selected route corresponding to the local route is reduced, or the pre-selected route is adjusted based on passenger orders corresponding to each pre-selected route passing through the local route. The method of adjusting the pre-selected route includes controlling the detour of vehicles passing through the pre-selected route from the station, and / or preventing vehicles that stop at the station from stopping at the station, and / or scheduling vehicles that transfer at other stations to transfer at the station based on the transfer strategy. If the average estimated waiting time is less than the upper limit of the first threshold range and the total estimated waiting time is greater than the upper limit of the second threshold range, then no scheduling will be performed. If the average estimated waiting time is greater than the upper limit of the first threshold range and the total estimated waiting time is less than the upper limit of the second threshold range, then no scheduling will be performed. The transfer strategy includes the following steps: Obtain the alternative path for the station corresponding to the feature value. Both the alternative path and the pre-selected path include a front segment, a middle segment, and a rear segment. The front and rear segments of the alternative path and the pre-selected path overlap. The station corresponding to the feature value is located in the middle segment of the pre-selected path. The middle segments of the alternative path and the pre-selected path are separate. If the average estimated waiting time is greater than the upper limit of the first threshold range, the total estimated waiting time is greater than the upper limit of the second threshold range, and the transfer points of passengers on the alternative route are not in the middle section of the alternative route, then the driving route of the vehicle on the alternative route is switched to the pre-selected route and stops at the station corresponding to the feature value. If the average estimated waiting time is less than the lower limit of the first threshold range, the total estimated waiting time is less than the lower limit of the second threshold range, and the transfer points of passengers on the pre-selected route are not located in the middle section of the pre-selected route, then the driving route of the vehicle on the pre-selected route will be switched to the backup route. During the journey, the system determines whether any passengers will get off at the next stop based on their passenger orders. If not, it determines whether any passengers need to board at the next stop. If not, the system does not stop at that stop. The route pre-selection step and the calculation step also include an allocation step, which includes the following steps: dividing the buses into short-distance buses and long-distance buses, and alternately dispatching short-distance buses and long-distance buses. Passenger orders whose origin is located at the station corresponding to the feature value are divided into short-distance orders and long-distance orders. Short-distance orders are those where the number of nodes between the transfer point / destination and the station is less than a preset threshold, and long-distance orders are those where the number of nodes between the transfer point / destination and the station is greater than a preset threshold. There are multiple preset thresholds, each corresponding to a different station. Determine if the remaining passenger capacity of the soon-to-arrive long-distance bus is greater than the number of long-distance orders. If it is, control the short-distance bus to only accept passengers corresponding to short-distance orders; otherwise, arrange the short-distance bus to accept passengers of both short-distance and long-distance orders. Determine if the remaining passenger capacity of the soon-to-arrive short-distance bus is greater than the number of short-distance orders. If it is, control the original-distance bus to only accept passengers corresponding to long-distance orders; otherwise, arrange for long-distance buses to accept passengers of both short-distance and long-distance orders.

2. An intelligent urban public transport management system, characterized in that, The intelligent urban public transport management method as described in claim 1 includes: The data information collection module is used to acquire passenger orders submitted by mobile devices, which include the origin and destination of the trip. The route planning module is used to obtain passenger orders and generate a combination of routes. The combination of routes includes multiple candidate routes. The starting point of the candidate route corresponds to the starting point of the passenger order's journey, and the ending point of the candidate route corresponds to the ending point of the passenger order's journey. The route push module is used to calculate the estimated time for each candidate route and push the candidate route and corresponding time to the mobile device; The path pre-selection module is used to obtain the pre-selected path submitted by the mobile terminal. The pre-selected path is a candidate path determined by the mobile terminal based on the combination of schemes. The calculation module is used to divide time intervals, obtain the number of orders at each station as the starting point of a local path in each time interval, and use the number of orders as the feature value of the local path. The local path is the path from each pre-selected path to the next node at this station. The node includes the starting point, transfer point and the ending point on the pre-selected path. The vehicle dispatching module is used to dispatch vehicles that pass near the station corresponding to the feature value and / or vehicles that pass on the local path corresponding to the feature value to stop or not stop at the station corresponding to the feature value, based on the feature value and the order information of the passengers on board.

3. An intelligent urban public transport system, characterized in that, The intelligent urban public transport management method as described in claim 1 includes a mobile terminal, a local or cloud-based server, buses, bus stops, and connecting vehicles, wherein the connecting vehicles are used to connect passengers from the departure point to the bus stop and to connect passengers from the alighting point to the final destination.

4. The intelligent urban public transport system according to claim 3, characterized in that, The mobile app includes a data collection module and an order payment and settlement module. The data collection module is used to obtain passenger orders submitted by the mobile app; the order payment and settlement module is used to generate the corresponding trip fee after the order is completed. The server includes at least a data information storage module and a data analysis and calculation module. The data information storage module is used to obtain passenger orders uploaded by the data information collection module, and to determine and record the final order after the trip. The data analysis and calculation module includes: The route planning and adjustment unit is used to schedule vehicles that pass near the station corresponding to the characteristic value and / or vehicles that pass on the local path corresponding to the characteristic value to stop or not stop at the station corresponding to the characteristic value, based on the characteristic value and the order information of the passengers on board. The vehicle dispatching and adjustment unit is used to adjust the departure frequency of vehicles based on characteristic values ​​and passenger order information. The connecting vehicle dispatching unit is used to adjust the number of connecting vehicles at the station corresponding to the characteristic value based on the characteristic value and the order information of the passengers on board.

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