技术领域Technical Field
本发明属于交通数据分析及处理领域,涉及一种基于多源数据的高速公路异常事件下的上游分流方法。The invention belongs to the field of traffic data analysis and processing, and relates to an upstream diversion method under abnormal events of a highway based on multi-source data.
背景技术Background technique
随着我国城市区域间经济互动逐渐深入,高速公路行车压力也在日渐变大。尤其是节假日免费通行、差异化收费等政策的实行,导致更多车辆涌入高速公路,供需矛盾更加凸显,高速公路中车辆行驶速度较快,流量大,且路网封闭性较强。一旦出现异常事件,拥堵扩散速度极快。当高速公路发生异常事件时,如果不能制定合理的交通组织方案及时有效疏解拥堵,那么拥堵很可能会进一步扩散,严重时会对整个路网造成影响。As the economic interaction between urban areas in my country gradually deepens, the pressure on highway traffic is also increasing. In particular, the implementation of policies such as free passage on holidays and differentiated charging has led to more vehicles pouring into highways, and the contradiction between supply and demand has become more prominent. Vehicles on highways travel at high speeds, with large traffic volumes and a strong closed road network. Once an abnormal event occurs, congestion spreads very quickly. When an abnormal event occurs on a highway, if a reasonable traffic organization plan cannot be formulated to effectively and timely relieve congestion, the congestion is likely to spread further, and in severe cases, it will affect the entire road network.
现有高速公路交通诱导分流多集中在高速公路入口分流。中国专利CN108665707A中,通过分析高速公路不同时刻每个出入口相邻路段内的交通需求量和通行能力,对每个路段的交通流是否达到饱和进行判断。并对交通需求大于通行能力的路段,通过电子信息板对即将进入收费站的车辆进行分流,通过核心控制策略来调节各入口最优交通量,该方法的适用于高速公路入口处的分流场景。Existing highway traffic induced diversion is mostly concentrated at the entrance of the highway. In Chinese patent CN108665707A, by analyzing the traffic demand and capacity of each entrance and exit of the highway at different times, it is judged whether the traffic flow of each section has reached saturation. For sections where the traffic demand is greater than the capacity, the electronic information board is used to divert vehicles about to enter the toll station, and the core control strategy is used to adjust the optimal traffic volume of each entrance. This method is suitable for diversion scenarios at the entrance of the highway.
目前对高速公路异常事件下的交通诱导分流研究较少,同时已有研究对于诱导分流启动条件、节点选取多采取定性分析的方法,还尚未形成一套完整的诱导分流体系。因此,制定完整的高速公路异常事件下的上游分流方案可以为交通管理人员组织应急交通提供支撑,便于其及时制定有效的应急组织方案,保障高速公路快速恢复畅行,因此需要提供一种基于多源数据的高速公路异常事件下的上游分流方法。At present, there are few studies on traffic induced diversion under abnormal events on highways. At the same time, existing studies have mostly adopted qualitative analysis methods for the start conditions and node selection of induced diversion, and have not yet formed a complete induced diversion system. Therefore, formulating a complete upstream diversion plan under abnormal events on highways can provide support for traffic management personnel to organize emergency traffic, facilitate them to formulate effective emergency organization plans in a timely manner, and ensure the rapid restoration of highway traffic. Therefore, it is necessary to provide an upstream diversion method under abnormal events on highways based on multi-source data.
发明内容Summary of the invention
本发明的目的在于提供一种基于多源数据的高速公路异常事件下的上游分流方法,以解决上述背景技术中提出的问题。The object of the present invention is to provide an upstream diversion method under abnormal events of highways based on multi-source data to solve the problems raised in the above background technology.
为实现上述目的,本发明提供如下技术方案:一种基于多源数据的高速公路异常事件下的上游分流方法,该方法包括以下五个步骤:To achieve the above object, the present invention provides the following technical solution: an upstream diversion method under abnormal highway events based on multi-source data, the method comprising the following five steps:
步骤1)明确方案目标函数,并制定完整的高速公路异常事件下的上游分流方案,具体包括以下几个子步骤:Step 1) Define the objective function of the scheme and formulate a complete upstream diversion scheme under abnormal highway events, which includes the following sub-steps:
步骤1.1:上游分流调控的目标是路网性能最优,总行程时间常被用来评价路网性能;Step 1.1: The goal of upstream diversion regulation is to optimize the road network performance. The total travel time is often used to evaluate the road network performance.
步骤1.2:完整的高速公路异常事件下的上游分流方案实施步骤如下;Step 1.2: The steps for implementing the upstream diversion plan under the complete highway abnormal event are as follows;
Step1:异常事件发生后,根据异常事件影响范围预测模型来判断是否达到启动诱导分流的条件;Step 1: After an abnormal event occurs, determine whether the conditions for initiating induced diversion are met based on the abnormal event impact range prediction model;
Step2:计算各匝道出口的分流节点优先级,确定分流节点;Step 2: Calculate the priority of the diversion nodes at each ramp exit and determine the diversion nodes;
Step3:根据断面ETC流量数据,统计目标时段的主线车流量;结合流量估计模型对目标时段主线流量进行估计;Step 3: According to the ETC flow data of the section, the main line traffic volume during the target period is counted; the main line traffic volume during the target period is estimated by combining the traffic estimation model;
Step4:根据Step3所估计流量以及异常事件影响范围预测模型,对异常事件路段行程时间进行估计,估计绕行路段行程时间,令调控方案目标函数最小,得到上游最佳分出流量;Step 4: Based on the estimated flow in Step 3 and the prediction model of the impact range of the abnormal event, estimate the travel time of the abnormal event section and the detour section, minimize the objective function of the control scheme, and obtain the optimal upstream diversion flow;
步骤2)选取合适的指标,定量化分析诱导分流启动条件,并制定确定诱导分流节点优先级,具体包括以下几个子步骤:Step 2) Select appropriate indicators, quantitatively analyze the conditions for inducing diversion, and determine the priority of the induced diversion nodes, which specifically includes the following sub-steps:
步骤2.1:诱导分流是否启动与拥堵的严重程度以及对路网的影响程度有关,最大排队长度可以用来表征拥堵的严重程度,排队最短消散时间可以用来表征异常事件对路网的影响程度,选取这两个指标来对是否启动诱导分流进行判断;Step 2.1: Whether induced diversion is activated is related to the severity of congestion and the impact on the road network. The maximum queue length can be used to represent the severity of congestion, and the shortest queue dissipation time can be used to represent the impact of abnormal events on the road network. These two indicators are selected to determine whether to activate induced diversion;
步骤2.2:流量饱和度可以反映绕行国省道的通行效率及路网的整体通行能力,绕行节省时间利于驾驶员选择行程时间更少的路径,因此选取绕行国省道流量饱和度和节省时间作为诱导分流节点选取的评价指标,并通过模糊综合评价法融合两个指标。Step 2.2: Traffic saturation can reflect the efficiency of bypassing national and provincial roads and the overall traffic capacity of the road network. The time saved by bypassing is conducive to drivers choosing a route with less travel time. Therefore, the traffic saturation and time saved of bypassing national and provincial roads are selected as evaluation indicators for the selection of induced diversion nodes, and the two indicators are integrated through the fuzzy comprehensive evaluation method.
步骤3)考虑异常事件的影响范围,对异常事件路段的行程时间进行估计,具体包括以下几个子步骤:Step 3) Considering the impact range of the abnormal event, the travel time of the abnormal event section is estimated, which specifically includes the following sub-steps:
步骤3.1:异常事件路段行程时间T主要由几个部分构成:车辆在队尾上游的行驶时间T1、车辆通过排队路段的时间T2、车辆通过瓶颈路段的时间T3和通过异常事件发生路段下游的时间T4,且有T=T1+T2+T3+T4;Step 3.1: The travel time T of the abnormal event section is mainly composed of several parts: the travel time T1 of the vehicle upstream of the queue, the time T2 of the vehicle passing through the queue section, the time T3 of the vehicle passing through the bottleneck section, and the time T4 of passing through the downstream of the abnormal event section, and T = T1 + T2 + T3 + T4 ;
步骤3.2:在排队还未蔓延到上游时,上游路段车辆行驶速度基本是不变的,车辆通过上游路段时间T1可以用下式表示:Step 3.2: Before the queue spreads to the upstream, the speed of vehicles on the upstream section is basically unchanged. The timeT1 for vehicles to pass through the upstream section can be expressed as follows:
式中,L5为瓶颈路段上游总长度;xt0为t0时刻的排队长度;V1为车辆在自由流情况下的行驶速度;Where L5 is the total length of the upstream of the bottleneck section; xt0 is the queue length at time t0 ; V1 is the speed of the vehicle under free flow conditions;
步骤3.3:车辆通过排队路段的行驶时间T2可用下式表示:Step 3.3: The travel timeT2 of a vehicle passing through the queue section can be expressed as follows:
式中,V2为车辆在排队路段的行驶速度;Where V2 is the speed of vehicles in the queue section;
步骤3.4:瓶颈路段长度一般包括因异常事件封闭车道长度和限制车辆驶入的区域长度;瓶颈路段车辆行驶速度一般不高于该路段的限速值,车辆通过该路段的行驶时间可以用下式表示:Step 3.4: The length of the bottleneck section generally includes the length of the lane closed due to abnormal events and the length of the area where vehicles are restricted from entering; the speed of vehicles in the bottleneck section is generally not higher than the speed limit of the section. The driving time of vehicles passing through the section can be expressed by the following formula:
式中,L3为瓶颈路段长度;V3为瓶颈路段限速值;Where, L3 is the length of the bottleneck section; V3 is the speed limit of the bottleneck section;
步骤3.5:下游路段不会受到异常事件影响,车辆行驶速度为自由流速度,车辆通过瓶颈路段下游的行驶时间可用下式表示:Step 3.5: The downstream section will not be affected by the abnormal event, and the vehicle speed is the free flow speed. The travel time of the vehicle passing through the downstream of the bottleneck section can be expressed as follows:
式中,L4为瓶颈路段下游路段长度;V1为车辆在自由流情况下的行驶速度;Where L4 is the length of the downstream section of the bottleneck section; V1 is the speed of the vehicle under free flow conditions;
步骤3.6:基于上述步骤,异常事件路段行程时间T可用下式表示:Step 3.6: Based on the above steps, the travel time T of the abnormal event section can be expressed as follows:
式中,L5为瓶颈路段上游总长度;xt0为t0时刻的排队长度;V1为车辆在自由流情况下的行驶速度;V2为车辆在排队路段的行驶速度;L3为瓶颈路段长度;V3为瓶颈路段限速值;L4为瓶颈路段下游路段长度;Where,L5 is the total length of the upstream of the bottleneck section; xt0 is the queue length at timet0 ;V1 is the speed of the vehicle under free flow conditions;V2 is the speed of the vehicle in the queue section;L3 is the length of the bottleneck section;V3 is the speed limit of the bottleneck section;L4 is the length of the downstream section of the bottleneck section;
步骤4)考虑到绕行路段的不确定性,确定一种通用的绕行路段行程时间估计方法,对绕行路段行程时间进行估计,具体包括以下几个子步骤:Step 4) Considering the uncertainty of the detour section, a general detour section travel time estimation method is determined to estimate the detour section travel time, which specifically includes the following sub-steps:
步骤4.1:确定适用于各等级公路的速度与流量关系公式;Step 4.1: Determine the speed-flow relationship formula applicable to each level of highway;
步骤4.2:根据步骤4.1可得不同流量下绕行路段的平均速度,从而可以得到不同流量下绕行路段的行程时间;Step 4.2: According to step 4.1, the average speed of the detour section under different flow rates can be obtained, and thus the travel time of the detour section under different flow rates can be obtained;
步骤5)根据异常事件路段及绕行路段原有流量,对异常事件路段最佳的分出流量进行估计,具体包括以下几个子步骤:Step 5) estimates the optimal outgoing flow rate of the abnormal event section based on the original flow rates of the abnormal event section and the detour section, which specifically includes the following sub-steps:
步骤5.1:通过一次指数平滑模型公式对异常事件路段车流量进行估计;Step 5.1: Estimate the traffic flow of the abnormal event section using a linear exponential smoothing model formula;
步骤5.2:从下道收费数据中提取出每个车辆的上道收费站信息,根据上道收费站和下道收费站的地理位置获取车辆的行车方向,进而可以得到每个方向的车流量,从而可以得到该匝道收费站的历史车流量上下行占比β;Step 5.2: Extract the information of the on-ramp toll station of each vehicle from the off-ramp toll data, obtain the driving direction of the vehicle according to the geographical locations of the on-ramp toll station and the off-ramp toll station, and then obtain the traffic flow in each direction, so as to obtain the historical traffic flow ratio β of the ramp toll station;
步骤5.3:利用步骤5.1中的公式对匝道收费站车流量进行估计,根据估计得到的流量及匝道收费站历史车流量上下行占比β对正常情况下目标时段内需要从高速公路主线下道的车流量进行估计,可用下式表示:Step 5.3: Use the formula in step 5.1 to estimate the traffic volume at the ramp toll station. Based on the estimated traffic volume and the up-and-down ratio β of the historical traffic volume at the ramp toll station, estimate the traffic volume that needs to go down the main line of the expressway during the target period under normal circumstances. It can be expressed by the following formula:
Qz=Qt+1·βQz =Qt+1 ·β
式中,Qz代表目标时段的匝道收费站流量;β代表匝道收费站历史车流量上下行占比;In the formula,Qz represents the ramp toll station flow rate during the target period; β represents the up-and-down ratio of the historical traffic flow rate at the ramp toll station;
步骤5.4:路网总行程时间可用下式表示,令TTT最小,即可得到异常事件路段的最佳分出流量:Step 5.4: The total travel time of the road network can be expressed as follows. By minimizing TTT, the optimal outflow flow of the abnormal event section can be obtained:
式中,q为高速公路主线流量;qy为绕行道路流量;qx为高速公路分到绕行道路的流量;Lr为绕行路段长度。Where q is the main line flow of the expressway;qy is the detour road flow;qx is the flow of the expressway allocated to the detour road;Lr is the length of the detour road section.
作为本发明的一种优选技术方案,所述步骤1)中分流方案的目标函数可用下述公式表示:As a preferred technical solution of the present invention, the objective function of the diversion solution in step 1) can be expressed by the following formula:
式中,ns为高速公路主线流量;nr为绕行道路流量;Ts为高速公路平均行程时间;Tr为绕行道路平均行程时间。Where ns is the traffic volume on the main line of the expressway; nr is the traffic volume on the detour road; Ts is the average travel time on the expressway;Tr is the average travel time on the detour road.
作为本发明的一种优选技术方案,所述步骤1)中的Step3的目标时段主线车流量的统计方法为:As a preferred technical solution of the present invention, the statistical method of the main line traffic volume in the target period of Step 3 in the step 1) is:
Step3a:从互通收费站下道流量提取出车辆上道收费站信息,根据上道收费站和下道收费站的地理位置获取车辆的行车方向,以天为时间间隔统计每天上下行方向车流量及其占比;Step 3a: Extract the vehicle on-road toll station information from the off-road traffic at the interchange toll station, obtain the vehicle's driving direction based on the geographical locations of the on-road toll station and the off-road toll station, and count the daily up- and down-road traffic flow and its proportion at daily intervals;
Step3b:根据目标时段主线流量以及收费站下道流量上、下行方向占比,对正常情况下目标时段内需要从高速公路主线下道的车流量进行估计。Step 3b: Based on the main line flow during the target period and the proportion of up and down traffic at the toll station, estimate the traffic flow that needs to go down the main line of the expressway during the target period under normal circumstances.
作为本发明的一种优选技术方案,所述步骤2.1)中的判别过程具体判别方法为:As a preferred technical solution of the present invention, the specific discrimination method of the discrimination process in step 2.1) is:
Step1:异常事件发生后,根据异常事件影响范围预测模型得到异常事件所导致的最大排队长度xmax及异常事件发生点交通状态恢复正常的时间tn;Step 1: After an abnormal event occurs, the maximum queue length xmax caused by the abnormal event and the time tn when the traffic state returns to normal at the point where the abnormal event occurs are obtained according to the abnormal event impact range prediction model;
Step2:根据异常事件属性,获取异常事件发生点距上游最近出口匝道的距离Lx;Step 2: According to the abnormal event attributes, obtain the distance Lx between the abnormal event occurrence point and the nearest upstream exit ramp;
Step3:若xmax<Lx,则不启动诱导分流,判别过程结束;Step 3: If xmax <Lx , the induced diversion is not started and the discrimination process ends;
Step4:若xmax>Lx且xmax>1.8km,则启动诱导分流,判别过程结束;Step 4: If xmax > Lx and xmax > 1.8 km, the induced diversion is started and the discrimination process ends;
Step5:若xmax>Lx但xmax<1.8km,需要进一步考虑tn的值;若tn<0.9h,则不启动诱导分流,判别过程结束;若tn>0.9h,则启动诱导分流,判别过程结束。Step 5: If xmax > Lx but xmax < 1.8 km, the value of tn needs to be further considered; if tn < 0.9 h, the induced diversion is not started and the discrimination process ends; if tn > 0.9 h, the induced diversion is started and the discrimination process ends.
其中,xmax为上游最大排队长度;Lx为异常事件发生点距上游最近出口匝道的距离;tn代表异常事件发生点恢复正常交通状态的时间。Among them, xmax is the maximum queue length upstream; Lx is the distance from the abnormal event occurrence point to the nearest upstream exit ramp; tn represents the time it takes for the abnormal event occurrence point to return to normal traffic conditions.
作为本发明的一种优选技术方案,所述步骤2.1)中的模糊评价规则如下:As a preferred technical solution of the present invention, the fuzzy evaluation rules in step 2.1) are as follows:
作为本发明的一种优选技术方案,所述步骤4.1中的各等级公路的速度与流量关系公式为:As a preferred technical solution of the present invention, the speed and flow relationship formula of each level of highway in step 4.1 is:
式中,v(q)为流量为q时的车速(km/h);vs为公路设计车速(km/h);q为交通量(veq/h);c为单条车道基本通行能力(pcu/h);vf为道路自由流车速;各回归参数取值为:α2=1.88,α3=4.90(高等级公路)或7.00(一般等级公路)。Wherein, v(q) is the vehicle speed when the flow rate is q (km/h);vs is the highway design speed (km/h); q is the traffic volume (veq/h); c is the basic traffic capacity of a single lane (pcu/h);vf is the free flow speed of the road; the values of each regression parameter are: α2 =1.88, α3 =4.90 (high-grade highway) or 7.00 (general-grade highway).
作为本发明的一种优选技术方案,所述步骤4.2中不同流量下绕行路段的形成时间的计算公式为:As a preferred technical solution of the present invention, the calculation formula for the formation time of the detour section under different flow rates in step 4.2 is:
式中,Tr为绕行道路平均行程时间;Lr为绕行道路路段长度;q为绕行道路流量;v(q)为流量为q时的车速(km/h)。WhereTr is the average travel time of the detour road;Lr is the length of the detour road section; q is the flow rate of the detour road; v(q) is the vehicle speed (km/h) when the flow rate is q.
作为本发明的一种优选技术方案,所述步骤5.1中的一次平滑指数模型公式为:As a preferred technical solution of the present invention, the first smoothing exponential model formula in step 5.1 is:
式中,Qt+1代表目标时段的流量;Qt代表当前时段的流量;代表目标时段的平均流量;α一般在0.1~0.3之间选取。In the formula, Qt+1 represents the flow rate in the target period; Qt represents the flow rate in the current period; Represents the average flow rate during the target period; α is generally selected between 0.1 and 0.3.
本发明的技术效果和优点:Technical effects and advantages of the present invention:
本发明提出一种基于多源数据的高速公路异常事件下的上游分流方法,该方法针对目前对高速公路异常事件下的交通诱导分流研究较少,同时已有研究对于诱导分流启动条件、节点选取多采取定性分析的方法,还尚未形成一套完整的诱导分流体系的现状,提出了高速公路异常事件下的上游分流调控方案,对诱导分流启动条件进行了定量化分析,并制定了确定分流节点优先级的方法,对实现分流方案所需要的交通参数进行了分析;本发明能够为交通管理者进行交通疏导提供重要的决策支持,便于其及时制定有效的应急组织方案,从而能够保障异常事件下高速公路快速恢复畅行。The present invention proposes an upstream diversion method under abnormal events of highways based on multi-source data. The method aims at the current situation that there are few studies on traffic induced diversion under abnormal events of highways, and the existing studies mostly adopt qualitative analysis methods for induced diversion starting conditions and node selection, and a complete induced diversion system has not yet been formed. The upstream diversion control scheme under abnormal events of highways is proposed, the induced diversion starting conditions are quantitatively analyzed, and a method for determining the priority of diversion nodes is formulated, and the traffic parameters required for implementing the diversion scheme are analyzed; the present invention can provide important decision-making support for traffic managers to conduct traffic diversion, facilitate them to formulate effective emergency organization plans in a timely manner, so as to ensure that highways can quickly resume smooth traffic under abnormal events.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明总体流程示意图;FIG1 is a schematic diagram of the overall process of the present invention;
图2为上游分流调控方案示意图;Figure 2 is a schematic diagram of the upstream diversion regulation scheme;
图3为诱导分流启动条件判别步骤;FIG3 is a step for determining the induction shunt start condition;
图4为异常事件路段行程时间组成;Figure 4 shows the travel time composition of the abnormal event section;
图5为收费站出口匝道流量获取流程图;Figure 5 is a flowchart of the flow acquisition of the toll station exit ramp;
具体实施方式Detailed ways
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。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
根据图1-5所示,本发明提供了一种基于多源数据的高速公路异常事件下的上游分流方法,一种基于多源数据的高速公路异常事件下的上游分流方法,该方法包括以下五个步骤:As shown in FIGS. 1-5 , the present invention provides an upstream diversion method under abnormal highway events based on multi-source data, an upstream diversion method under abnormal highway events based on multi-source data, the method comprising the following five steps:
步骤1)明确方案目标函数,并制定完整的高速公路异常事件下的上游分流方案,具体包括以下几个子步骤:Step 1) Define the objective function of the scheme and formulate a complete upstream diversion scheme under abnormal highway events, which includes the following sub-steps:
步骤1.1:上游分流调控的目标是路网性能最优,总行程时间常被用来评价路网性能;Step 1.1: The goal of upstream diversion regulation is to optimize the road network performance. The total travel time is often used to evaluate the road network performance.
步骤1.2:完整的高速公路异常事件下的上游分流方案实施步骤如下:Step 1.2: The steps for implementing the upstream diversion plan under a complete highway abnormal event are as follows:
Step1:异常事件发生后,根据异常事件影响范围预测模型来判断是否达到启动诱导分流的条件;Step 1: After an abnormal event occurs, determine whether the conditions for initiating induced diversion are met based on the abnormal event impact range prediction model;
Step2:计算各匝道出口的分流节点优先级,确定分流节点;Step 2: Calculate the priority of the diversion nodes at each ramp exit and determine the diversion nodes;
Step3:根据断面ETC流量数据,统计目标时段的主线车流量;结合流量估计模型对目标时段主线流量进行估计;Step 3: According to the ETC flow data of the section, the main line traffic volume during the target period is counted; the main line traffic volume during the target period is estimated by combining the traffic estimation model;
Step4:根据Step3所估计流量以及异常事件影响范围预测模型,对异常事件路段行程时间进行估计,估计绕行路段行程时间,令调控方案目标函数最小,得到上游最佳分出流量。Step 4: Based on the estimated flow in Step 3 and the prediction model of the impact range of abnormal events, estimate the travel time of the abnormal event section and the detour section, minimize the objective function of the control plan, and obtain the optimal upstream diversion flow.
步骤2)选取合适的指标,定量化分析诱导分流启动条件,并制定确定诱导分流节点优先级,具体包括以下几个子步骤:Step 2) Select appropriate indicators, quantitatively analyze the conditions for inducing diversion, and determine the priority of the induced diversion nodes, which specifically includes the following sub-steps:
步骤2.1:诱导分流是否启动与拥堵的严重程度以及对路网的影响程度有关,最大排队长度可以用来表征拥堵的严重程度,排队最短消散时间可以用来表征异常事件对路网的影响程度,选取这两个指标来对是否启动诱导分流进行判断;Step 2.1: Whether induced diversion is activated is related to the severity of congestion and the impact on the road network. The maximum queue length can be used to represent the severity of congestion, and the shortest queue dissipation time can be used to represent the impact of abnormal events on the road network. These two indicators are selected to determine whether to activate induced diversion;
步骤2.2:流量饱和度可以反映绕行国省道的通行效率及路网的整体通行能力,绕行节省时间利于驾驶员选择行程时间更少的路径,因此选取绕行国省道流量饱和度和节省时间作为诱导分流节点选取的评价指标,并通过模糊综合评价法融合两个指标。Step 2.2: Traffic saturation can reflect the efficiency of bypassing national and provincial roads and the overall traffic capacity of the road network. The time saved by bypassing is conducive to drivers choosing a route with less travel time. Therefore, the traffic saturation and time saved of bypassing national and provincial roads are selected as evaluation indicators for the selection of induced diversion nodes, and the two indicators are integrated through the fuzzy comprehensive evaluation method.
步骤3)考虑异常事件的影响范围,对异常事件路段的行程时间进行估计,具体包括以下几个子步骤:Step 3) Considering the impact range of the abnormal event, the travel time of the abnormal event section is estimated, which specifically includes the following sub-steps:
步骤3.1:异常事件路段行程时间T主要由几个部分构成:车辆在队尾上游的行驶时间T1、车辆通过排队路段的时间T2、车辆通过瓶颈路段的时间T3和通过异常事件发生路段下游的时间T4,且有T=T1+T2+T3+T4;Step 3.1: The travel time T of the abnormal event section is mainly composed of several parts: the travel time T1 of the vehicle upstream of the queue, the time T2 of the vehicle passing through the queue section, the time T3 of the vehicle passing through the bottleneck section, and the time T4 of passing through the downstream of the abnormal event section, and T = T1 + T2 + T3 + T4 ;
步骤3.2:在排队还未蔓延到上游时,上游路段车辆行驶速度基本是不变的,车辆通过上游路段时间T1可以用下式表示:Step 3.2: Before the queue spreads to the upstream, the speed of vehicles on the upstream section is basically unchanged. The timeT1 for vehicles to pass through the upstream section can be expressed as follows:
式中,L5为瓶颈路段上游总长度;xt0为t0时刻的排队长度;V1为车辆在自由流情况下的行驶速度。WhereL5 is the total length of the upstream of the bottleneck section;xt0 is the queue length at timet0 ;V1 is the speed of the vehicle under free flow conditions.
步骤3.3:车辆通过排队路段的行驶时间T2可用下式表示:Step 3.3: The travel timeT2 of a vehicle passing through the queue section can be expressed as follows:
式中,V2为车辆在排队路段的行驶速度;Where V2 is the speed of vehicles in the queue section;
步骤3.4:瓶颈路段长度一般包括因异常事件封闭车道长度和限制车辆驶入的区域长度;瓶颈路段车辆行驶速度一般不高于该路段的限速值,车辆通过该路段的行驶时间可以用下式表示:Step 3.4: The length of the bottleneck section generally includes the length of the lane closed due to abnormal events and the length of the area where vehicles are restricted from entering; the speed of vehicles in the bottleneck section is generally not higher than the speed limit of the section. The driving time of vehicles passing through the section can be expressed by the following formula:
式中,L3为瓶颈路段长度;V3为瓶颈路段限速值;Where, L3 is the length of the bottleneck section; V3 is the speed limit of the bottleneck section;
步骤3.5:下游路段不会受到异常事件影响,车辆行驶速度为自由流速度,车辆通过瓶颈路段下游的行驶时间可用下式表示:Step 3.5: The downstream section will not be affected by the abnormal event, and the vehicle speed is the free flow speed. The travel time of the vehicle passing through the downstream of the bottleneck section can be expressed as follows:
式中,L4为瓶颈路段下游路段长度;V1为车辆在自由流情况下的行驶速度。WhereL4 is the length of the downstream section of the bottleneck section;V1 is the speed of the vehicle under free flow conditions.
步骤3.6:基于上述步骤,异常事件路段行程时间T可用下式表示:Step 3.6: Based on the above steps, the travel time T of the abnormal event section can be expressed as follows:
式中,L5为瓶颈路段上游总长度;xt0为t0时刻的排队长度;V1为车辆在自由流情况下的行驶速度;V2为车辆在排队路段的行驶速度;L3为瓶颈路段长度;V3为瓶颈路段限速值;L4为瓶颈路段下游路段长度。Where,L5 is the total length of the upstream of the bottleneck section;xt0 is the queue length at timet0 ;V1 is the speed of the vehicle under free flow conditions;V2 is the speed of the vehicle in the queue section;L3 is the length of the bottleneck section;V3 is the speed limit of the bottleneck section;L4 is the length of the downstream section of the bottleneck section.
步骤4)考虑到绕行路段的不确定性,确定一种通用的绕行路段行程时间估计方法,对绕行路段行程时间进行估计,具体包括以下几个子步骤:Step 4) Considering the uncertainty of the detour section, a general detour section travel time estimation method is determined to estimate the detour section travel time, which specifically includes the following sub-steps:
步骤4.1:确定适用于各等级公路的速度与流量关系公式;Step 4.1: Determine the speed-flow relationship formula applicable to each level of highway;
步骤4.2:根据步骤4.1可得不同流量下绕行路段的平均速度,从而可以得到不同流量下绕行路段的行程时间。Step 4.2: According to step 4.1, the average speed of the detour section under different flow rates can be obtained, and thus the travel time of the detour section under different flow rates can be obtained.
步骤5)根据异常事件路段及绕行路段原有流量,对异常事件路段最佳的分出流量进行估计,具体包括以下几个子步骤:Step 5) estimates the optimal outgoing flow rate of the abnormal event section based on the original flow rates of the abnormal event section and the detour section, which specifically includes the following sub-steps:
步骤5.1:通过一次指数平滑模型公式对异常事件路段车流量进行估计;Step 5.1: Estimate the traffic flow of the abnormal event section using a linear exponential smoothing model formula;
步骤5.2:从下道收费数据中提取出每个车辆的上道收费站信息,根据上道收费站和下道收费站的地理位置获取车辆的行车方向,进而可以得到每个方向的车流量,从而可以得到该匝道收费站的历史车流量上下行占比β。Step 5.2: Extract the information of the on-ramp toll station for each vehicle from the off-ramp toll data, obtain the driving direction of the vehicle based on the geographical locations of the on-ramp toll station and the off-ramp toll station, and then obtain the traffic flow in each direction, so as to obtain the historical up-and-down ratio β of the traffic flow at the ramp toll station.
步骤5.3:利用步骤5.1中的公式对匝道收费站车流量进行估计,根据估计得到的流量及匝道收费站历史车流量上下行占比β对正常情况下目标时段内需要从高速公路主线下道的车流量进行估计,可用下式表示:Step 5.3: Use the formula in step 5.1 to estimate the traffic volume at the ramp toll station. Based on the estimated traffic volume and the up-and-down ratio β of the historical traffic volume at the ramp toll station, estimate the traffic volume that needs to go down the main line of the expressway during the target period under normal circumstances. It can be expressed by the following formula:
Qz=Qt+1·βQz =Qt+1 ·β
式中,Qz代表目标时段的匝道收费站流量;β代表匝道收费站历史车流量上下行占比。WhereQz represents the ramp toll station flow rate during the target period; β represents the up-and-down ratio of the historical traffic flow rate at the ramp toll station.
步骤5.4:路网总行程时间可用下式表示,令TTT最小,即可得到异常事件路段的最佳分出流量:Step 5.4: The total travel time of the road network can be expressed as follows. By minimizing TTT, the optimal outflow flow of the abnormal event section can be obtained:
式中,q为高速公路主线流量;qy为绕行道路流量;qx为高速公路分到绕行道路的流量;Lr为绕行路段长度。Where q is the main line flow of the expressway;qy is the detour road flow;qx is the flow of the expressway allocated to the detour road;Lr is the length of the detour road section.
作为本发明的一种优选实施例,所述步骤1)中分流方案的目标函数可用下述公式表示:As a preferred embodiment of the present invention, the objective function of the diversion scheme in step 1) can be expressed by the following formula:
式中,ns为高速公路主线流量;nr为绕行道路流量;Ts为高速公路平均行程时间;Tr为绕行道路平均行程时间。Where ns is the traffic volume on the main line of the expressway; nr is the traffic volume on the detour road; Ts is the average travel time on the expressway;Tr is the average travel time on the detour road.
进一步地,所述步骤1)中的Step3的目标时段主线车流量的统计方法为:Furthermore, the statistical method of the main line traffic volume in the target period of Step 3 in Step 1) is:
Step3a:从互通收费站下道流量提取出车辆上道收费站信息,根据上道收费站和下道收费站的地理位置获取车辆的行车方向,以天为时间间隔统计每天上下行方向车流量及其占比;Step 3a: Extract the vehicle on-road toll station information from the off-road traffic at the interchange toll station, obtain the vehicle's driving direction based on the geographical locations of the on-road toll station and the off-road toll station, and count the daily up- and down-road traffic flow and its proportion at daily intervals;
Step3b:根据目标时段主线流量以及收费站下道流量上、下行方向占比,对正常情况下目标时段内需要从高速公路主线下道的车流量进行估计。Step 3b: Based on the main line flow during the target period and the proportion of up and down traffic at the toll station, estimate the traffic flow that needs to go down the main line of the expressway during the target period under normal circumstances.
作为本发明的一种优选实施例,所述步骤2.1)中的判别过程具体判别方法为:As a preferred embodiment of the present invention, the specific determination method of the determination process in step 2.1) is:
Step1:异常事件发生后,根据异常事件影响范围预测模型得到异常事件所导致的最大排队长度xmax及异常事件发生点交通状态恢复正常的时间tn;Step 1: After an abnormal event occurs, the maximum queue length xmax caused by the abnormal event and the time tn when the traffic state returns to normal at the point where the abnormal event occurs are obtained according to the abnormal event impact range prediction model;
Step2:根据异常事件属性,获取异常事件发生点距上游最近出口匝道的距离Lx;Step 2: According to the abnormal event attributes, obtain the distance Lx between the abnormal event occurrence point and the nearest upstream exit ramp;
Step3:若xmax<Lx,则不启动诱导分流,判别过程结束;Step 3: If xmax <Lx , the induced diversion is not started and the discrimination process ends;
Step4:若xmax>Lx且xmax>1.8km,则启动诱导分流,判别过程结束;Step 4: If xmax > Lx and xmax > 1.8 km, the induced diversion is started and the discrimination process ends;
Step5:若xmax>Lx但xmax<1.8km,需要进一步考虑tn的值;若tn<0.9h,则不启动诱导分流,判别过程结束;若tn>0.9h,则启动诱导分流,判别过程结束。Step 5: If xmax > Lx but xmax < 1.8 km, the value of tn needs to be further considered; if tn < 0.9 h, the induced diversion is not started and the discrimination process ends; if tn > 0.9 h, the induced diversion is started and the discrimination process ends.
其中,xmax为上游最大排队长度;Lx为异常事件发生点距上游最近出口匝道的距离;tn代表异常事件发生点恢复正常交通状态的时间。Among them, xmax is the maximum queue length upstream; Lx is the distance from the abnormal event occurrence point to the nearest upstream exit ramp; tn represents the time it takes for the abnormal event occurrence point to return to normal traffic conditions.
进一步地,所述步骤2.1)中的模糊评价规则如下:Furthermore, the fuzzy evaluation rules in step 2.1) are as follows:
作为本发明的一种优选实施例,所述步骤4.1中的各等级公路的速度与流量关系公式为:As a preferred embodiment of the present invention, the speed and flow relationship formula of each level of highway in step 4.1 is:
式中,v(q)为流量为q时的车速(km/h);vs为公路设计车速(km/h);q为交通量(veq/h);c为单条车道基本通行能力(pcu/h);vf为道路自由流车速;各回归参数取值为:α2=1.88,α3=4.90(高等级公路)或7.00(一般等级公路)。Wherein, v(q) is the vehicle speed when the flow rate is q (km/h);vs is the highway design speed (km/h); q is the traffic volume (veq/h); c is the basic traffic capacity of a single lane (pcu/h);vf is the free flow speed of the road; the values of each regression parameter are: α2 =1.88, α3 =4.90 (high-grade highway) or 7.00 (general-grade highway).
进一步地,所述步骤4.2中不同流量下绕行路段的形成时间的计算公式为:Furthermore, the calculation formula for the formation time of the detour section under different flow rates in step 4.2 is:
式中,Tr为绕行道路平均行程时间;Lr为绕行道路路段长度;q为绕行道路流量;v(q)为流量为q时的车速(km/h)。WhereTr is the average travel time of the detour road;Lr is the length of the detour road section; q is the flow rate of the detour road; v(q) is the vehicle speed (km/h) when the flow rate is q.
作为本发明的另一种优选实施例,所述步骤5.1中的一次平滑指数模型公式为:式中,Qt+1代表目标时段的流量;Qt代表当前时段的流量;/>代表目标时段的平均流量;α一般在0.1~0.3之间选取。As another preferred embodiment of the present invention, the linear smoothing exponential model formula in step 5.1 is: In the formula, Qt+1 represents the flow rate in the target period; Qt represents the flow rate in the current period; /> Represents the average flow rate during the target period; α is generally selected between 0.1 and 0.3.
综上所述,本发明的方法针对目前对高速公路异常事件下的交通诱导分流研究较少,同时已有研究对于诱导分流启动条件、节点选取多采取定性分析的方法,还尚未形成一套完整的诱导分流体系的现状,提出了高速公路异常事件下的上游分流调控方案,对诱导分流启动条件进行了定量化分析,并制定了确定分流节点优先级的方法,对实现分流方案所需要的交通参数进行了分析;本发明能够为交通管理者进行交通疏导提供重要的决策支持,便于其及时制定有效的应急组织方案,从而能够保障异常事件下高速公路快速恢复畅行。In summary, the method of the present invention aims at the current situation that there are few studies on traffic induced diversion under abnormal events on highways. At the same time, the existing studies mostly adopt qualitative analysis methods for the starting conditions and node selection of induced diversion, and a complete induced diversion system has not yet been formed. An upstream diversion control scheme under abnormal events on highways is proposed, the starting conditions of induced diversion are quantitatively analyzed, and a method for determining the priority of diversion nodes is formulated, and the traffic parameters required to implement the diversion scheme are analyzed; the present invention can provide important decision-making support for traffic managers to conduct traffic diversion, facilitate them to formulate effective emergency organization plans in a timely manner, so as to ensure that highways can quickly resume smooth traffic under abnormal events.
尽管已经示出和描述了本发明的实施例,对于本领域的普通技术人员而言,可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型,本发明的范围由所附权利要求及其等同物限定。Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115662145B (en)* | 2022-12-26 | 2023-03-10 | 广东汇通信息科技股份有限公司 | Emergency shunting method for vehicles on accident road section based on big data |
| CN116311897A (en)* | 2022-12-30 | 2023-06-23 | 阿波罗智联(北京)科技有限公司 | Traffic guidance method, device, electronic equipment and medium |
| CN118314721B (en)* | 2024-03-25 | 2025-01-14 | 北京交通大学 | Road traffic system risk assessment method and system under major emergency |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001167381A (en)* | 1999-12-07 | 2001-06-22 | Sumitomo Electric Ind Ltd | Exit traffic jam detection method, exit traffic jam detection system, exit traffic jam detection device, and recording medium |
| JP2003044975A (en)* | 2001-07-31 | 2003-02-14 | I Transport Lab Co Ltd | Simulation apparatus, simulation method, and simulation program |
| WO2010042973A1 (en)* | 2008-10-15 | 2010-04-22 | National Ict Australia Limited | Tracking the number of vehicles in a queue |
| CN105023433A (en)* | 2015-07-01 | 2015-11-04 | 重庆大学 | Method for predicting range influenced by abnormal traffic event of highway |
| CN108665707A (en)* | 2018-05-24 | 2018-10-16 | 金陵科技学院 | It is a kind of prevent local traffic congestion expressway access shunting and flow cooperative control method |
| CN109410599A (en)* | 2017-08-17 | 2019-03-01 | 南京洛普股份有限公司 | The coordination of expressway ramp lures prosecutor method under a kind of traffic events |
| CN111161537A (en)* | 2019-12-25 | 2020-05-15 | 北京交通大学 | Road congestion situation prediction method considering congestion superposition effect |
| CN111724590A (en)* | 2020-06-03 | 2020-09-29 | 重庆大学 | Estimation method of abnormal event occurrence time of expressway based on travel time correction |
| CN111724595A (en)* | 2020-06-23 | 2020-09-29 | 重庆大学 | A method of expressway section flow estimation based on toll data |
| CN111724589A (en)* | 2020-06-03 | 2020-09-29 | 重庆大学 | A method for estimating freeway cross-section flow based on multi-source data |
| CN111882858A (en)* | 2020-06-01 | 2020-11-03 | 重庆大学 | A Queue Length Prediction Method for Expressway Abnormal Events Based on Multi-source Data |
| CN111915880A (en)* | 2020-06-05 | 2020-11-10 | 重庆首讯科技股份有限公司 | Method for estimating effective traffic capacity of accident point of highway abnormal event |
| CN112542047A (en)* | 2020-12-07 | 2021-03-23 | 东南大学 | Emergency induced diversion discrimination method for expressway traffic accidents |
| CN112581774A (en)* | 2020-12-08 | 2021-03-30 | 重庆大学 | ETC data-based method for estimating traffic flow of driving vehicles in expressway service area |
| CN112767684A (en)* | 2020-12-22 | 2021-05-07 | 重庆大学 | Highway traffic jam detection method based on charging data |
| CN112927503A (en)* | 2021-01-25 | 2021-06-08 | 河北上元智能科技股份有限公司 | Method for coordinating and controlling main line speed limitation and ramp fusion of expressway in rainy days |
| CN112991724A (en)* | 2021-02-09 | 2021-06-18 | 重庆大学 | Method and device for estimating occurrence position and occurrence time of highway abnormal event |
| CN113781815A (en)* | 2021-08-31 | 2021-12-10 | 湖南湘旭交安光电高科技股份有限公司 | Intersection dynamic detour induction control method and device under cooperative vehicle and road environment and storage medium |
| CN114141030A (en)* | 2021-12-27 | 2022-03-04 | 吉林大学 | A post-accident expressway traffic induction method under heterogeneous driving demands |
| CN114333305A (en)* | 2021-12-21 | 2022-04-12 | 北京掌行通信息技术有限公司 | Vehicle induced passing method and device during highway congestion, storage medium and terminal |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2343435C (en)* | 2001-04-06 | 2006-12-05 | International Road Dynamics Inc. | Dynamic work zone safety system |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001167381A (en)* | 1999-12-07 | 2001-06-22 | Sumitomo Electric Ind Ltd | Exit traffic jam detection method, exit traffic jam detection system, exit traffic jam detection device, and recording medium |
| JP2003044975A (en)* | 2001-07-31 | 2003-02-14 | I Transport Lab Co Ltd | Simulation apparatus, simulation method, and simulation program |
| WO2010042973A1 (en)* | 2008-10-15 | 2010-04-22 | National Ict Australia Limited | Tracking the number of vehicles in a queue |
| CN105023433A (en)* | 2015-07-01 | 2015-11-04 | 重庆大学 | Method for predicting range influenced by abnormal traffic event of highway |
| CN109410599A (en)* | 2017-08-17 | 2019-03-01 | 南京洛普股份有限公司 | The coordination of expressway ramp lures prosecutor method under a kind of traffic events |
| CN108665707A (en)* | 2018-05-24 | 2018-10-16 | 金陵科技学院 | It is a kind of prevent local traffic congestion expressway access shunting and flow cooperative control method |
| CN111161537A (en)* | 2019-12-25 | 2020-05-15 | 北京交通大学 | Road congestion situation prediction method considering congestion superposition effect |
| CN111882858A (en)* | 2020-06-01 | 2020-11-03 | 重庆大学 | A Queue Length Prediction Method for Expressway Abnormal Events Based on Multi-source Data |
| CN111724589A (en)* | 2020-06-03 | 2020-09-29 | 重庆大学 | A method for estimating freeway cross-section flow based on multi-source data |
| CN111724590A (en)* | 2020-06-03 | 2020-09-29 | 重庆大学 | Estimation method of abnormal event occurrence time of expressway based on travel time correction |
| CN111915880A (en)* | 2020-06-05 | 2020-11-10 | 重庆首讯科技股份有限公司 | Method for estimating effective traffic capacity of accident point of highway abnormal event |
| CN111724595A (en)* | 2020-06-23 | 2020-09-29 | 重庆大学 | A method of expressway section flow estimation based on toll data |
| CN112542047A (en)* | 2020-12-07 | 2021-03-23 | 东南大学 | Emergency induced diversion discrimination method for expressway traffic accidents |
| CN112581774A (en)* | 2020-12-08 | 2021-03-30 | 重庆大学 | ETC data-based method for estimating traffic flow of driving vehicles in expressway service area |
| CN112767684A (en)* | 2020-12-22 | 2021-05-07 | 重庆大学 | Highway traffic jam detection method based on charging data |
| CN112927503A (en)* | 2021-01-25 | 2021-06-08 | 河北上元智能科技股份有限公司 | Method for coordinating and controlling main line speed limitation and ramp fusion of expressway in rainy days |
| CN112991724A (en)* | 2021-02-09 | 2021-06-18 | 重庆大学 | Method and device for estimating occurrence position and occurrence time of highway abnormal event |
| CN113781815A (en)* | 2021-08-31 | 2021-12-10 | 湖南湘旭交安光电高科技股份有限公司 | Intersection dynamic detour induction control method and device under cooperative vehicle and road environment and storage medium |
| CN114333305A (en)* | 2021-12-21 | 2022-04-12 | 北京掌行通信息技术有限公司 | Vehicle induced passing method and device during highway congestion, storage medium and terminal |
| CN114141030A (en)* | 2021-12-27 | 2022-03-04 | 吉林大学 | A post-accident expressway traffic induction method under heterogeneous driving demands |
| Title |
|---|
| Disscussion on Emergency Traffic Organization Programs of Expressway Traffic Accident under Circumstance of Road Network;Chang Zhenwen等;2009 Second International Conference on Intelligent Computation Technology and Automation;20091016;全文* |
| 分车型的高速公路短时交通流量预测方法;周桐等;计算机应用研究;20150731(第07期);全文* |
| 高速公路事故持续时间及影响范围研究;王伟;中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑;20180215(第02期);全文* |
| 高速公路异常事件下考虑上游匝道流量的排队长度预测;黄启亮;中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑;20220415(第04期);全文* |
| Publication number | Publication date |
|---|---|
| CN115424432A (en) | 2022-12-02 |
| Publication | Publication Date | Title |
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