WO2021004115A1 - 一种人工驾驶公交和自动驾驶公交联合调度优化方法 - Google Patents
一种人工驾驶公交和自动驾驶公交联合调度优化方法 Download PDFInfo
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- the invention relates to the technical field of intelligent traffic information processing, and more specifically, to a method for joint dispatching and optimization of a manual driving bus and an automatic driving bus.
- Bus travel presents a general morning and evening peak passenger demand situation, in which the peak passenger demand in the morning and evening peak is large, and the peak passenger demand is small.
- the public transportation department is facing huge challenges in vehicle scheduling and management, that is, how to ensure the quality of service during peak and peak periods under certain operating costs, such as shortening the waiting time of passengers on platforms and reducing congestion in the car.
- bus dispatching mainly adopts the following two methods to deal with the peak and peace peak travel of passengers: (1) Divide peak and peace peak periods, and formulate peak and peace peak vehicle scheduling schedules respectively, where the frequency of bus departure during peak hours is greater than that during peak periods.
- This method controls operating costs by increasing the frequency of vehicle scheduling during peak hours and reducing the frequency of vehicle scheduling during peak hours, and guarantees the service quality during peak travel to the greatest extent; however, this method reduces the frequency of departures during peak periods and is bound to extend passenger platform waiting during peak periods Time; (2) Develop a bus dispatch plan for demand response.
- This method uses historical passenger demand data, vehicle GPS data and passenger IC card swiping data to predict passenger travel demand in a period of time in the future, and uses this as a basis to formulate a dynamic bus dispatch plan. Compared with method 1, this method has more advantages in terms of operating cost control and improvement of service quality.
- this method may have the problem of wasting vehicle fuel consumption during peak periods, such as low passenger pick-up rate and effective vehicles
- the utilization rate is low; during peak periods, due to the fixed capacity of public buses, passengers may not be able to board the bus.
- autonomous buses have unique advantages in further improving the demand responsiveness and scheduling flexibility of urban public transport systems.
- Existing studies point out that autonomous buses It can improve road driving safety, reduce the total fuel consumption of the bus system, reduce the driver's labor cost, and optimize the bus travel time to reduce bus skew.
- self-driving bus units with smaller capacity, dynamic adjustment of the vehicle capacity of self-driving buses can be realized. For example, during peak hours, multiple self-driving bus units are combined to form a bus fleet, thereby increasing vehicle capacity.
- each self-driving bus unit is dispatched separately, and ultimately reduce operating costs without reducing the frequency of bus departures. Therefore, as far as the public transportation management department is concerned, it can not only improve the quality of service by optimizing the frequency of bus departures, but also optimize the capacity of self-driving buses to further reduce operating costs.
- the present invention provides a joint scheduling optimization method of manual driving and automatic driving buses, which ensures the full utilization of manual driving and automatic driving buses, improves service quality, and saves operating costs.
- a joint scheduling optimization method for manually driven buses and autonomous buses includes:
- Step 1 Discretize the scheduling period into uniformly distributed time nodes, and set decision variables; wherein, the decision variables are the departure types of different time nodes;
- Step 2 Establish a bus operation simulation model based on the number of buses, the time of leaving the station, the time of passengers getting on and off, the demand for getting on the bus, the actual number of passengers, the number of people getting off the bus, the number of people stuck at the station and the number of people on the bus;
- Step 3 Set up the operating cost functions of manual driving and autonomous driving buses
- Step 4 Determine the cost of waiting time for passengers
- Step 5 Establish a joint dispatching optimization model for manual and autonomous buses based on the operating cost function and the cost of passenger waiting time;
- Step 6 Solve the optimization model, and obtain a joint dispatching scheme of manual driving and autonomous driving.
- step one specifically includes:
- the decision variable x mk is the type of departure at different time nodes, and x mk is a 0-1 variable, indicating whether to send a bus of type m at time node k.
- step two specifically includes:
- N 0 is the number of existing manned bus, N a number of conventional bus autopilot;
- the departure time d v,1 and departure type ⁇ v of all vehicles are:
- ⁇ is the unit time length after discretization time
- the time interval between the two connected vehicles leaving the initial platform is not less than h 0 :
- Departure time is expressed as the departure time of the vehicle at the previous platform plus the travel time of the bus between the two platforms, plus the time of passengers getting on and off the bus at the current platform:
- n s represents the number of platforms of the bus line
- ⁇ b and ⁇ a are the average time taken for a passenger to board and get off, respectively, Is the actual number of passengers, ⁇ v, s is the number of people getting off;
- the boarding demand ⁇ v,s includes the passengers who arrive at the station during the bus driving process and the passengers who cannot board the bus because the preceding vehicle is full ⁇ v-1,s ,
- ⁇ s is the passenger arrival rate at platform s
- d v,s -d v-1,s is the headway of vehicle v at platform s
- the boarding demand ⁇ v,s and the actual number of people and vehicles The difference is the number of passengers left by vehicle v at platform s
- the ratio of the number of getting off the bus v at platform s to the actual number of passengers on board is ⁇ s , then the number of getting off the bus v at platform s is
- step three specifically includes:
- c 0 is the capacity of manually driven buses, with Respectively represent the fixed operating cost and marginal operating cost of manually driven buses;
- the operating cost is expressed as Where mc is the vehicle capacity of the self-driving bus model m, with Indicates the fixed operating cost and marginal operating cost of autonomous buses.
- the passenger waiting time includes two parts, one part is the time for passengers to wait for the first arriving bus after arriving at the station, and the other part is the time for the passengers to wait longer due to vehicle capacity restrictions that cannot board the bus;
- the average waiting time of passengers is half of the headway, namely The total number of arriving passengers is ⁇ s (d v,s -d v-1,s ), so when the vehicle v arrives at platform s, the waiting time for passengers on this platform is
- the passenger waiting time is the product of the number of stranded passengers ⁇ v,s and the headway time.
- the optimization model is:
- ⁇ 1 and ⁇ 2 are the cost parameters corresponding to the two parts of waiting time.
- the optimization model is a nonlinear shaping optimization model, which is directly solved by commercial optimization software Cplex or gurobi.
- the present disclosure provides a method for optimizing the joint dispatching of a manually driven bus and an automatic driving bus, which has the following advantages:
- the present invention fully considers the influence of the variable capacity design of the autonomous driving bus on the traditional bus dispatch, and can reduce the operating cost of the management department by adjusting the bus capacity without reducing the frequency of bus departures.
- the present invention discretizes the scheduling cycle time and sets the decision variable as the departure type of each time node.
- This modeling method realizes the joint scheduling of manual driving and autonomous driving, and simultaneously optimizes the departure of public buses according to passenger needs Frequency and vehicle capacity, the resulting scheduling plan is extremely flexible, suitable for the flexible scheduling needs of modern high-capacity public transportation systems.
- the present invention abstracts the manual driving bus and the automatic driving bus into different models, which can be characterized by the same decision variable, simplifies the optimization model, improves the calculation efficiency, and is better suitable for modeling and optimization of the actual complex bus dispatching system .
- Fig. 1 is a flow chart of a method for optimizing the joint scheduling of a manual driving bus and an automatic driving bus provided by the present invention
- Fig. 2 is a schematic diagram of the method for optimizing the joint dispatching of a manual driving bus and an automatic driving bus provided by the present invention.
- the embodiment of the present invention discloses a joint scheduling optimization method of a manual driving bus and an automatic driving bus, including:
- Step 1 Discretize the scheduling period into uniformly distributed time nodes, and set decision variables; wherein, the decision variables are the departure types of different time nodes;
- Step 2 Establish a bus operation simulation model based on the number of buses, the time of leaving the station, the time of passengers getting on and off, the demand for getting on the bus, the actual number of passengers, the number of people getting off the bus, the number of people stuck at the station and the number of people on the bus;
- Step 3 Set up the operating cost functions of manual driving and autonomous driving buses
- Step 4 Determine the cost of waiting time for passengers
- Step 5 Establish a joint dispatching optimization model for manual and autonomous buses based on the operating cost function and the cost of passenger waiting time;
- Step 6 Solve the optimization model, and obtain a joint dispatching scheme of manual driving and autonomous driving.
- step three and step four is not limited, as long as it is completed before step five is executed.
- step one specifically includes:
- the decision variable x mk is the type of departure at different time nodes, and x mk is a 0-1 variable, indicating whether to send a bus of type m at time node k.
- step two specifically includes:
- N 0 is the number of existing manned bus, N a number of conventional bus autopilot;
- the departure time d v,1 and departure type ⁇ v of all vehicles are:
- ⁇ is the unit time length after discretization time
- the number of the preceding vehicle is v-1.
- the minimum departure time interval is set to h 0 , and h 0 can be set according to the road characteristics of the bus line and passenger demand. Therefore, the time interval between the two connected vehicles leaving the initial platform is not less than h 0 :
- Departure time is expressed as the departure time of the vehicle at the previous platform plus the travel time of the bus between the two platforms, plus the time of passengers getting on and off the bus at the current platform:
- n s represents the number of platforms of the bus line
- ⁇ b and ⁇ a are the average time taken for a passenger to board and get off, respectively, Is the actual number of passengers, ⁇ v, s is the number of people getting off;
- the boarding demand ⁇ v,s includes the passengers who arrive at the station during the bus driving process and the passengers who cannot board the bus because the preceding vehicle is full ⁇ v-1,s ,
- ⁇ s is the passenger arrival rate at platform s
- d v,s -d v-1,s is the headway of vehicle v at platform s
- the boarding demand ⁇ v,s and the actual number of people and vehicles The difference is the number of passengers left by vehicle v at platform s
- the ratio of the number of getting off the bus v at platform s to the actual number of passengers on board is ⁇ s , then the number of getting off the bus v at platform s is
- l v,1 0 means that the initial number of passengers carried by the vehicle is 0; formulas (1) to (14) are recursively obtained in order to obtain the operation process and passenger number status of all vehicles in the dispatch period, and calculate the bus operating cost and Cost of waiting time for passengers.
- step three specifically includes:
- c 0 is the capacity of manually driven buses, with Respectively represent the fixed operating cost and marginal operating cost of manually driven buses;
- the operating cost is expressed as Where mc is the vehicle capacity of the self-driving bus model m, with Indicates the fixed operating cost and marginal operating cost of autonomous buses.
- the passenger waiting time includes two parts, one part is the time when passengers wait for the first arriving bus after arriving at the station, and the other part is the time when passengers cannot board the bus due to vehicle capacity restrictions.
- the passenger waiting time is the product of the number of stranded passengers ⁇ v,s and the headway time.
- step 5 the optimization model is:
- ⁇ 1 and ⁇ 2 are the cost parameters corresponding to the two parts of waiting time.
- the optimization model is a nonlinear shaping optimization model, which is directly solved by commercial optimization software Cplex or gurobi.
- the present invention provides a method for optimizing the joint dispatching of a manual driving bus and an automatic driving bus.
- the available models are first determined.
- the manual driving bus is model 0 and the capacity is c 0 ;
- the automatic driving bus is realized by assembling (or disassembling) the automatic driving bus unit
- Capacity adjustment can be used to obtain autopilot bus models 1, 2,...,a, where model a represents an autopilot bus assembled from a autopilot bus unit, and the capacity is ac, where c is the passenger capacity of the autopilot bus unit ;
- the decision variable is the departure type of the first time node.
- the objective function is optimized as the bus operation cost The sum of passenger cost, where passenger cost is the cost of passenger waiting time.
- the technical solution provided by the present invention is based on the development situation of autonomous driving buses, analyzes its impact on existing bus dispatching, and fully considers the possibility of the variable capacity characteristics of autonomous driving buses for improving the quality of bus services and reducing operating costs;
- the joint scheduling optimization method dynamically adjusts the capacity and frequency of autonomous driving buses according to passenger demand, shortens the waiting time of passengers, and reduces the risk of passengers not being able to board the bus during peak periods; for public transportation management departments, the joint scheduling optimization method It ensures the full use of manual driving and self-driving buses, improves scheduling efficiency, and saves operating costs by dynamically adjusting the capacity of self-driving buses during peak and peak periods.
- the simulated bus line contains 10 bus stations, the distance between the stations is 500m, and the travel time between the stations obeys a lognormal distribution (lognormal), where the mean is 1 minute and the variance coefficient is 0.4.
- Scenario 1 Traditional bus dispatch based on manual driving bus.
- the system has 4 manually driven buses with a vehicle capacity of 45 seats per vehicle, totaling 180 seats.
- the fixed cost of manually driven buses is 350 yuan/shift, and the marginal cost is 4 yuan/seat. From the cost function (15), the operating cost of manually driven buses is 530 yuan/shift.
- Scenario 2 Joint dispatching of manually driven buses and autonomous buses.
- the system has 2 manually-driven buses with a vehicle capacity of 45 seats/car; 15 self-driving bus units with a vehicle capacity of 6 seats/unit; a total of 180 seats for both manually-driven buses and autonomous buses.
- the autonomous bus can assemble up to 5 autonomous bus units.
- the available vehicle types for the autonomous bus include: Model 1 (6 seats/car), Model 2 (12 seats/car), Model 3 (18 Seats/car), model 4 (24 seats/car) and model 5 (30 seats/car). Since autonomous buses do not require driver intervention, the fixed cost of autonomous buses is 130 yuan/shift, and the marginal cost is 4 yuan/seat.
- the operating costs of the five autonomous bus types are 154 yuan/shift, 178 yuan/shift, 202 yuan/shift, 226 yuan/shift, and 250 yuan/shift.
- the departure frequency is 5 minutes/shift, which ensures that all vehicles can be fully utilized; in scenario 2, the departure frequency and vehicle type are calculated from the optimization model (16); model (16) is adopted Cplex software optimization. Carry out 20 simulations, each simulation is 5 hours, take the average of 20 simulation results as the final result. The simulation results are shown in Table 1.
- the present invention jointly dispatches the manual driving bus and the automatic driving bus, and jointly optimizes the departure frequency and vehicle capacity.
- the method can effectively reduce the bus operating cost (a reduction of 7.0%) and passengers The cost of waiting time (reduction by 14.9%), and at the same time reducing the average waiting time of passengers (reduction by 21.1%).
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Abstract
本发明公开了一种人工驾驶公交和自动驾驶公交联合调度优化方法,充分考虑了自动驾驶公交的容量可变特性对提高公交服务质量和减少运营成本的可能性;对乘客而言,该联合调度优化方法根据乘客需求动态调整自动驾驶公交容量和发车频率,缩短了乘客候车时间,降低了在高峰期乘客不能上车的风险;对公交管理部门而言,该联合调度优化方法保证了人工驾驶公交和自动驾驶公交的充分利用,提升了调度效率,通过在高峰和平峰期动态调整自动驾驶公交容量节约了运营成本。
Description
本发明涉及智能交通信息处理技术领域,更具体的说是涉及一种人工驾驶公交和自动驾驶公交联合调度优化方法。
公交出行呈现普遍的早晚高峰乘客需求态势,其中早晚高峰乘客需求大,平峰乘客需求小。在此背景下,公交部门面临车辆调度和管理的巨大挑战,即如何在一定运营成本条件下保证高峰期和平峰期的服务质量,如缩短乘客站台等待时间和减小车内拥挤等。当前,公交车辆调度主要采用以下两种方式来应对乘客的高峰和平峰出行:(1)划分高峰和平峰时段,并分别制定高峰和平峰车辆调度时刻表,其中高峰时段公交发车频率大于平峰时段。该方法通过提高高峰时段车辆调度频率和降低平峰时段车辆调度频率来控制运营成本,且最大程度保证高峰出行时段的服务质量;然而该方法降低了平峰期发车频率,势必会延长平峰期乘客站台候车时间;(2)制定需求响应的公交调度方案。该方法通过历史乘客需求数据、车辆GPS数据及乘客IC卡刷卡数据预测未来一段时间内的乘客出行需求,并以此为据制定动态的公交车辆调度方案。与方法一相比,该方法在运营成本控制和提高服务质量方面有更多的优势,然而由于公交车辆容量固定,在平峰期该方法可能存在车辆油耗浪费的问题,如上客率低,车辆有效利用率低等;在高峰期间,由于公交车辆容量固定,乘客也存在不能上车的风险。
随着车辆传感、人工智能和车联网等技术的发展,自动驾驶公交(autonomous bus)在进一步提高城市公交***的需求响应性和调度灵活性上 有独特的优势,现有研究指出自动驾驶公交能够提高道路驾驶安全、减少公交***总油耗、减少司机人力成本、及最优化公交行程时间以减少公交串车等。此外,通过连接或组装容量较小的自动驾驶公交单元,能够实现自动驾驶公交的车辆容量动态调整,如在高峰期多个自动驾驶公交单元组合在一起,形成公交车队,从而提高车辆容量,减少乘客站台候车时间;在平峰期,各自动驾驶公交单元则分开调度,最终在不减小公交发车频率条件下降低运营成本。因此,对公交管理部门而言,其不仅能通过优化公交发车频率来提高服务质量,还能同时优化自动驾驶公交车辆容量来进一步减小运营成本。
与此同时,完全自动驾驶技术由于技术不成熟、安全性和政府法规等问题被预测需要较长时间才能完全占有市场。在未来一段时间内,人工驾驶公交和自动驾驶公交并存的可能性较大。
因此,如何高效调度人工驾驶公交和自动驾驶公交,以提升服务质量,减少运营成本的是本领域技术人员亟需解决的问题。
发明内容
有鉴于此,本发明提供了一种人工驾驶公交和自动驾驶公交联合调度优化方法,保证了人工驾驶公交和自动驾驶公交的充分利用,提升了服务质量,节约了运营成本。
为了实现上述目的,本发明采用如下技术方案:
一种人工驾驶公交和自动驾驶公交联合调度优化方法,包括:
步骤一:将调度周期离散化为均匀分布的时间节点,并设置决策变量;其中,所述决策变量为不同时间节点的发车类型;
步骤二:基于发车数量、车辆离站时间、乘客上下车时间、上车需求、实际上车人数、下车人数、滞站人数和车上人数建立公交车辆运行仿真模型;
步骤三:设置人工驾驶公交和自动驾驶公交运营成本函数;
步骤四:确定乘客候车时间成本;
步骤五:基于运营成本函数和乘客候车时间成本建立人工驾驶公交和自动驾驶公交联合调度优化模型;
步骤六:对优化模型进行求解,得到人工驾驶公交和自动驾驶公交联合调度方案。
优选的,步骤一具体包括:
将调度周期T离散化为n
k+1个均匀分布的时间节点,离散化时间节点表示为κ=[0,1,...,n
k],则单位离散时间长度为δ=T/n
k;
决策变量x
mk为不同时间节点的发车类型,x
mk为0-1变量,表示是否在时间节点k发出一辆类型为m的公交车辆。
优选的,步骤二具体包括:
在每一时间节点、至多有一辆公交车从站台发出,且总发车数量不超过现有公交数量:
其中,N
0为现有人工驾驶公交数量,N
a为现有自动驾驶公交数量;
根据决策变量x
mk求得所有车辆的发车时间d
v,1和发车类型θ
v为:
其中,δ为离散化时间后的单位时间长度;
相连两车离开初始站台的时间间隔不小于h
0:
d
v,1-d
v-1,1≥h
0 v=2,...,n (7)
设公交车辆v在站台s和站台s+1的行程时间为t
v,s,在站台s的离站时间为d
v,s,乘客上下车时间为u
v,s,则公交车辆在站台的离站时间表示为该车辆在前一站台的离站时间加上公交在两站台间的行程时间,再加上公交在当前站台的乘客上下车时间:
d
v,s=d
v,s-1+t
v,s-1+u
v,s v=1,...,n;s=2,...,n
s (8)
其中,n
s表示公交线路的站台数量;
对于公交***而言,乘客通过车辆前后门同时上下车,乘客上下车时间为乘客上车和下车耗时的最大值:
上车需求β
v,s包括在公交行驶过程中到站的乘客和由于前车车满而不能上车的乘客ω
v-1,s,
β
v,s=ω
v-1,s+λ
s(d
v,s-d
v-1,s) v=1,...,n;s=1,...,n
s-1 (10)
其中λ
s为站台s的乘客到达率,d
v,s-d
v-1,s为车辆v在站台s的车头时距;
根据公交车辆在所有站台的下车乘客数量历史统计,得到车辆v在站台s的下车人数与实际车载人数比值为ρ
s,则车辆v在站台s的下车人数为
α
v,s=ρ
sl
v,s v=1,...,n;s=2,...,n
s (13)
最后,求得车辆v到达站台s时的载客人数l
v,s为该车辆到达前一站台时的载客人数加上一站实际上车人数,减去上一站下车人数,即
其中l
v,1=0表示车辆初始载客人数为0。
优选的,步骤三具体包括:
所有车型的运营成本为:
优选的,步骤四中,乘客候车时间包括两部分,一部分为乘客到站后等待第一辆到站公交车的时间,另一部分为由于车辆容量限制乘客不能上车而多等待的时间;对于第一部分,设定乘客随机到达,乘客平均候车时间为车头时距的一半,即
而总到达乘客数量为λ
s(d
v,s-d
v-1,s),故车辆v到站台s时该站台乘客候车时间为
对于第二部分,乘客候车时间为滞留乘客人数ω
v,s和车头时距之积。
优选的,在步骤五中,优化模型为:
其中,ρ
1和ρ
2分别为两部分候车时间对应的成本参数。
优选的,所述优化模型为非线性整形优化模型,通过商业优化软件Cplex或gurobi直接求解。
经由上述的技术方案可知,与现有技术相比,本发明公开提供了一种人工驾驶公交和自动驾驶公交联合调度优化方法,具有如下优点:
1、本发明充分考虑了自动驾驶公交可变容量设计对传统公交调度的影响,可实现在不降低公交发车频率条件下,通过调整公交容量减少管理部门运营成本。
2、本发明将调度周期时间离散化,并设置决策变量为每一时间节点的发车类型,该建模方法实现了人工驾驶公交和自动驾驶公交的联合调度,且依据乘客需求同时优化公交车辆发车频率和车辆容量,所得调度方案极其灵活,适用于现代高容量公交***灵活调度的需求。
3、本发明将人工驾驶公交和自动驾驶公交抽象为不同车型,可通过同一决策变量表征,简化了优化模型,提高了计算效率,可更好适用于现实复杂公交车辆调度***的建模和优化。
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据提供的附图获得其他的附图。
图1为本发明提供的一种人工驾驶公交和自动驾驶公交联合调度优化方法流程图;
图2为本发明提供的人工驾驶公交和自动驾驶公交联合调度优化方法示意图。
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。
参见附图1和附图2,本发明实施例公开了一种人工驾驶公交和自动驾驶公交联合调度优化方法,包括:
步骤一:将调度周期离散化为均匀分布的时间节点,并设置决策变量;其中,所述决策变量为不同时间节点的发车类型;
步骤二:基于发车数量、车辆离站时间、乘客上下车时间、上车需求、实际上车人数、下车人数、滞站人数和车上人数建立公交车辆运行仿真模型;
步骤三:设置人工驾驶公交和自动驾驶公交运营成本函数;
步骤四:确定乘客候车时间成本;
步骤五:基于运营成本函数和乘客候车时间成本建立人工驾驶公交和自动驾驶公交联合调度优化模型;
步骤六:对优化模型进行求解,得到人工驾驶公交和自动驾驶公交联合调度方案。
这里需要说明的是,步骤三和步骤四的执行顺序并做不限定,只要在步骤五执行前完成即可。
为了进一步优化上述技术方案,步骤一具体包括:
将调度周期T离散化为n
k+1个均匀分布的时间节点,离散化时间节点表示为κ=[0,1,...,n
k],则单位离散时间长度为δ=T/n
k;
决策变量x
mk为不同时间节点的发车类型,x
mk为0-1变量,表示是否在时间节点k发出一辆类型为m的公交车辆。
为了进一步优化上述技术方案,步骤二具体包括:
在每一时间节点、至多有一辆公交车从站台发出,且总发车数量不超过现有公交数量:
其中,N
0为现有人工驾驶公交数量,N
a为现有自动驾驶公交数量;
根据决策变量x
mk求得所有车辆的发车时间d
v,1和发车类型θ
v为:
其中,δ为离散化时间后的单位时间长度;
对公交车辆v而言,其前车编号为v-1。为了保证车辆调度的安全性,设置最小发车时间间隔为h
0,h
0可根据公交线路的道路特征和乘客需求设置。因此,相连两车离开初始站台的时间间隔不小于h
0:
d
v,1-d
v-1,1≥h
0 v=2,...,n (7)
设公交车辆v在站台s和站台s+1的行程时间为t
v,s,在站台s的离站时间为d
v,s,乘客上下车时间为u
v,s,则公交车辆在站台的离站时间表示为该车辆在 前一站台的离站时间加上公交在两站台间的行程时间,再加上公交在当前站台的乘客上下车时间:
d
v,s=d
v,s-1+t
v,s-1+u
v,s v=1,...,n;s=2,...,n
s (8)
其中,n
s表示公交线路的站台数量;
对于公交***而言,乘客通过车辆前后门同时上下车,乘客上下车时间为乘客上车和下车耗时的最大值:
上车需求β
v,s包括在公交行驶过程中到站的乘客和由于前车车满而不能上车的乘客ω
v-1,s,
β
v,s=ω
v-1,s+λ
s(d
v,s-d
v-1,s) v=1,...,n;s=1,...,n
s-1 (10)
其中λ
s为站台s的乘客到达率,d
v,s-d
v-1,s为车辆v在站台s的车头时距;
根据公交车辆在所有站台的下车乘客数量历史统计,得到车辆v在站台s的下车人数与实际车载人数比值为ρ
s,则车辆v在站台s的下车人数为
α
v,s=ρ
sl
v,s v=1,...,n;s=2,...,n
s (13)
最后,求得车辆v到达站台s时的载客人数l
v,s为该车辆到达前一站台时的载客人数加上一站实际上车人数,减去上一站下车人数,即
其中l
v,1=0表示车辆初始载客人数为0;公式(1)到(14)依次递推,得到所有车辆在调度周期内的运行过程和乘客数量状态,据此计算公交运营成本和乘客候车时间成本。
为了进一步优化上述技术方案,步骤三具体包括:
所有车型的运营成本为:
为了进一步优化上述技术方案,步骤四中,乘客候车时间包括两部分,一部分为乘客到站后等待第一辆到站公交车的时间,另一部分为由于车辆容量限制乘客不能上车而多等待的时间;对于第一部分,设定乘客随机到达,乘客平均候车时间为车头时距的一半,即
而总到达乘客数量为λ
s(d
v,s-d
v-1,s),故车辆v到站台s时该站台乘客候车时间为
对于第二部分,乘客候车时间为滞留乘客人数ω
v,s和车头时距之积。
为了进一步优化上述技术方案,在步骤五中,优化模型为:
其中,ρ
1和ρ
2分别为两部分候车时间对应的成本参数。
为了进一步优化上述技术方案,所述优化模型为非线性整形优化模型,通过商业优化软件Cplex或gurobi直接求解。
本发明提供的一种人工驾驶公交和自动驾驶公交联合调度优化方法,先确定可用车型,其中人工驾驶公交为车型0,容量为c
0;自动驾驶公交通过组装(或拆卸)自动驾驶公交单元实现容量调整,可得自动驾驶公交车型1,2,...,a,其中车型a表示a个自动驾驶公交单元组装所得的自动驾驶公交,容量为ac,其中c为自动驾驶公交单元乘客容量;则可用车型可表示为m∈M=[-1,0,1,2,...,a],其中m=-1为不发车情形,m=0为人工驾驶公交发车情形,m>0为自动驾驶公交发车情形。进一步,将公交调度周期时间离散化(如每分钟)。设决策变量为第一时间节点的发车类型,通过优化该决策变量,可实现公交发车频率和车辆容量的动态调整,且同时实现人工驾驶公交和自动驾驶公交联合调度,优化目标函数为公交运营成本和乘客成本之和,其中乘客成本为乘客候车时间成本。
本发明提供的技术方案立足自动驾驶公交发展形势,分析其对现有公交调度的影响,充分考虑了自动驾驶公交的容量可变特性对提高公交服务质量和减少运营成本的可能性;对乘客而言,该联合调度优化方法根据乘客需求动态调整自动驾驶公交容量和发车频率,缩短了乘客候车时间,降低了在高峰期乘客不能上车的风险;对公交管理部门而言,该联合调度优化方法保证了人工驾驶公交和自动驾驶公交的充分利用,提升了调度效率,通过在高峰和平峰期动态调整自动驾驶公交容量节约了运营成本。
下面结合一个仿真实例来说明本发明所述的一种人工驾驶公交和自动驾驶公交联合调度优化方法。仿真公交线路包含10个公交站台,站台间距为500m,车辆在站台间的行驶时间服从对数正态分布(lognormal),其中均值为1分钟,方差变异系数为0.4。考虑以下两种仿真场景。
场景一:基于人工驾驶公交的传统公交调度。设***有人工驾驶公交4辆,车辆容量为45座/车,共计180座。人工驾驶公交固定成本为350元/班次,边际成本为4元/座,由成本函数(15)可知人工驾驶公交的运营成本为530元/班次。
场景二:人工驾驶公交和自动驾驶公交联合调度。设***有人工驾驶公交2辆,车辆容量为45座/车;自动驾驶公交单元15个,车辆容量为6座/单元;人工驾驶公交和自动驾驶公交共计180座。基于驾驶安全性,设自动驾驶公交最多可以组装5个自动驾驶公交单元,则自动驾驶公交可用车辆类型包括:车型1(6座/车),车型2(12座/车),车型3(18座/车),车型4(24座/车)和车型5(30座/车)。由于自动驾驶公交无需司机干预,设自动驾驶公交固定成本为130元/班次,边际成本为4元/座。则5种自动驾驶公交类型的运营成本分别为154元/班次、178元/班次、202元/班次、226元/班次和250元/班次。乘客候车时间成本参数ρ
1=5元/分钟,ρ
2=7元/分钟。
在场景一中,发车频率为5分钟/班次,该发车频率保证了所有车辆能得到充分利用;在场景二中,发车频率和发车车辆类型取优化模型(16)计算结果;模型(16)采用Cplex软件优化。进行20次仿真,每次仿真为5小时,取20次仿真结果均值为最后结果。仿真结果如表1所示。
表1 仿真结果
运营成本(元) | 乘客成本(元) | 乘客平均候车时间(分钟) | |
场景一 | 25248 | 63588 | 3.23 |
场景二 | 23483 | 54110 | 2.55 |
模型提升 | 7.0% | 14.9% | 21.1% |
本发明将人工驾驶公交和自动驾驶公交联合调度,并对发车频率和车辆容量进行联合优化,与传统基于人工驾驶公交的调度相比,该方法能有效降低公交运营成本(降低7.0%)和乘客候车时间成本(降低14.9%),且同时降低乘客平均候车时间(降低21.1%)。
本说明书中各个实施例采用递进的方式描述,每个实施例重点说明的都是与其他实施例的不同之处,各个实施例之间相同相似部分互相参见即可。对于实施例公开的装置而言,由于其与实施例公开的方法相对应,所以描述的比较简单,相关之处参见方法部分说明即可。
对所公开的实施例的上述说明,使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的,本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下,在其它实施例中实现。因此,本发明将不会被限制于本文所示的这些实施例,而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。
Claims (7)
- 一种人工驾驶公交和自动驾驶公交联合调度优化方法,其特征在于,包括:步骤一:将调度周期离散化为均匀分布的时间节点,并设置决策变量;其中,所述决策变量为不同时间节点的发车类型;步骤二:基于发车数量、车辆离站时间、乘客上下车时间、上车需求、实际上车人数、下车人数、滞站人数和车上人数建立公交车辆运行仿真模型;步骤三:设置人工驾驶公交和自动驾驶公交运营成本函数;步骤四:确定乘客候车时间成本;步骤五:基于运营成本函数和乘客候车时间成本建立人工驾驶公交和自动驾驶公交联合调度优化模型;步骤六:对优化模型进行求解,得到人工驾驶公交和自动驾驶公交联合调度方案。
- 根据权利要求1所述的一种人工驾驶公交和自动驾驶公交联合调度优化方法,其特征在于,步骤一具体包括:将调度周期T离散化为n k+1个均匀分布的时间节点,离散化时间节点表示为κ=[0,1,...,n k],则单位离散时间长度为δ=T/n k;决策变量x mk为不同时间节点的发车类型,x mk为0-1变量,表示是否在时间节点k发出一辆类型为m的公交车辆。
- 根据权利要求2所述的一种人工驾驶公交和自动驾驶公交联合调度优化方法,其特征在于,步骤二具体包括:在每一时间节点、至多有一辆公交车从站台发出,且总发车数量不超过现有公交数量:其中,N 0为现有人工驾驶公交数量,N a为现有自动驾驶公交数量;根据决策变量x mk求得所有车辆的发车时间d v,1和发车类型θ v为:其中,δ为离散化时间后的单位时间长度;相连两车离开初始站台的时间间隔不小于h 0:d v,1-d v-1,1≥h 0 v=2,...,n (7)设公交车辆v在站台s和站台s+1的行程时间为t v,s,在站台s的离站时间为d v,s,乘客上下车时间为u v,s,则公交车辆在站台的离站时间表示为该车辆在前一站台的离站时间加上公交在两站台间的行程时间,再加上公交在当前站台的乘客上下车时间:d v,s=d v,s-1+t v,s-1+u v,s v=1,...,n;s=2,...,n s (8)其中,n s表示公交线路的站台数量;对于公交***而言,乘客通过车辆前后门同时上下车,乘客上下车时间为乘客上车和下车耗时的最大值:上车需求β v,s包括在公交行驶过程中到站的乘客和由于前车车满而不能上车的乘客ω v-1,s,β v,s=ω v-1,s+λ s(d v,s-d v-1,s) v=1,...,n;s=1,...,n s-1 (10)其中λ s为站台s的乘客到达率,d v,s-d v-1,s为车辆v在站台s的车头时距;根据公交车辆在所有站台的下车乘客数量历史统计,得到车辆v在站台s的下车人数与实际车载人数比值为ρ s,则车辆v在站台s的下车人数为α v,s=ρ sl v,s v=1,...,n;s=2,...,n s (13)最后,求得车辆v到达站台s时的载客人数l v,s为该车辆到达前一站台时的载客人数加上一站实际上车人数,减去上一站下车人数,即其中l v,1=0表示车辆初始载客人数为0。
- 根据权利要求1~6任意一项所述的一种人工驾驶公交和自动驾驶公交联合调度优化方法,其特征在于,所述优化模型为非线性整形优化模型,通过商业优化软件Cplex或gurobi直接求解。
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