WO2018103449A1 - 基于建成环境和低频浮动车数据的路段行程时间估计方法 - Google Patents

基于建成环境和低频浮动车数据的路段行程时间估计方法 Download PDF

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WO2018103449A1
WO2018103449A1 PCT/CN2017/105633 CN2017105633W WO2018103449A1 WO 2018103449 A1 WO2018103449 A1 WO 2018103449A1 CN 2017105633 W CN2017105633 W CN 2017105633W WO 2018103449 A1 WO2018103449 A1 WO 2018103449A1
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segment
road
running time
point
time
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钟绍鹏
隽海民
邹延权
王坤
朱康丽
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Dalian University of Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination

Definitions

  • the invention belongs to the technical field of urban traffic management and transportation system evaluation, relates to the ITS intelligent transportation system and the ATIS traveler information system, and particularly relates to the explanation of the travel time of the built environment and the estimation method of the travel time of the road section.
  • Liu H X uses the floating car data combined with the traditional coil data and signal phase information to propose a method for signal control on the road travel time prediction; Hellinga B studied how the running time of the floating car between the two reports is assigned to the corresponding On the road segment, each observed total travel time is divided into free flow time, control parking delay, and congestion delay; Rahmani M et al. directly estimate the running time based on the path, and propose a travel time estimation method without parameter estimation.
  • the running track of the floating car that coincides with the research path, it is considered that the running speeds of the path and the floating car track are consistent, and the time taken by the path and the floating car track to pass through each road segment is proportional to the distance traveled on the road segment.
  • the technical problem to be solved by the present invention is to estimate the travel time distribution between the road segments and the road segments by using the distribution of the number of vehicles on the road segments, and to establish a travel time history database, which can be used as a road segment running time allocation coefficient instead of the distance.
  • the method for estimating the travel time of the road segment based on the built environment and the low frequency floating car data is as follows:
  • the possibility that the floating car sends a report is greater.
  • the event that the floating car sends the report is used as a random variable, and the detected number of reported reports of the floating car at each point is established. The relationship between point runs time.
  • the time interval for the floating car to send the report is fixed.
  • the probability of each floating car sending the report at any time is the same.
  • the probability that the floating car sends the report at each moment is ⁇ .
  • T is the time interval between the two reports sent by the floating car
  • ⁇ is the frequency at which the floating car sends the report
  • the road segment is divided into several segments, and the running time of each segment depends on the observed and unobserved segment attributes.
  • the segment attributes include the distance of the segment from the downstream intersection, the distance from the crosswalk, and the section.
  • the attributes of the segment to which the segment belongs (such as lane width, number of lanes, geometric line, etc.). Special consideration is given to the influence of pedestrians entering and leaving the vehicle on the road segment or the entry and exit of the motor vehicle to form a construction environment with particularly large mutual interference between the vehicles.
  • a linear structure is used to represent the explanatory variables associated with the run time of the segment (regulatory factors such as road grade, geometrical linearity of the road segment, nearby land use) and the effect of the length of a particular segment on the segment run time t'(x).
  • X represents the road segment
  • x represents one of the segments
  • a j represents the value of the explanatory variable affecting the running time of the segment, such as the road grade, the distance from the downstream intersection, etc.
  • ⁇ j represents the running time of each explanatory variable to the segment The extent of the impact is the parameter to be estimated.
  • observation value of the path running time is t ok
  • k represents a certain runtime observation
  • K represents all runtime observations.
  • the observation running time of each section is the sum of the running times of each section.
  • the relationship between the observed road segment and the segment can be represented by a K ⁇ X correlation matrix R, where each element r kx represents the ratio of the distance of each observation k through each segment x to the total distance of the segment.
  • the linear combination method is used to establish the relationship between the running time of the road segment and the intersection and the built environment. system. It is then estimated that the runtime of each segment translates into a maximum likelihood estimation problem:
  • ⁇ j is the parameter to be estimated
  • m is the estimated total number of vehicles
  • n x is the number of vehicles transmitting the report.
  • the estimated result is the value of each parameter, and You can find the running time of each segment. Then according to the correlation matrix of the road segment and the segment, the running time of the road segment can be obtained.
  • the total running time on the road segment is the integral of the running time t"(x) along each point of the road segment.
  • a certain running time in the road section is the integral of the running time along each point of the section, that is,
  • the observed number nx of vehicles reporting their position at this point is the desired unbiased estimate.
  • the running time of the floating car at this point is proportional to the probability that the floating car will report its position at each point on the road segment. Therefore, it can be considered that the running time of the floating car at this point is proportional to the number of times the floating car reports its position at each point on the road segment. That is, t(x) ⁇ p(x) ⁇ E(x) ⁇ n x .
  • the road segment can be segmented, and the total number of times the vehicle reports the position during each period in a certain time interval is counted.
  • the ratio of the running time of each segment to the total running time of the road segment is equal to the total number of reports sent by the vehicle and the entire road segment.
  • ⁇ 1 represents the ratio of the running time of the first segment to the total running time of the road segment
  • t 1 represents the running time of the first segment
  • l 1 , l 2 represent the starting point of the first and second segments
  • L represents the end point of the last segment.
  • the vehicle passes an arbitrary repeated test at any position of two or more road segments.
  • the ratio of the running times of the two sections is obtained based on the ratio of the total number of times the vehicles passing through the two sections send reports on the two sections:
  • T 1 and T 2 respectively represent the running time of the two road segments
  • L 1 and L 2 respectively represent the lengths of the two road segments, so that the ratio between all the road segments is obtained, and the problem of the running time allocation between the road segments is solved.
  • the beneficial effects of the invention adding the built environment as an explanatory variable of the running time of the road section, and proving the interpretation of the running environment for the running time of the road section; including the running time of the intersection in the travel time of the road section, and taking the distance from the intersection as the road section travel
  • the explanatory variables of time can effectively consider the impact of traffic management and control facilities at the intersection on the running time.
  • a method for estimating the distribution coefficient of travel time between road segments and road segments by using the distribution of the number of vehicles on the road segment is also given. It is used to establish the travel time history database as the road running time distribution coefficient and improve the accuracy of the travel time estimation result of the road segment. .
  • the method for estimating the travel time of the road segment based on the built environment and the low frequency floating car data is as follows:
  • the influence of the design level, geometric linearity, and number of lanes of each section on the running time is set as a parameter, which is equivalent to the running time of the section in the study period when it is far away from the intersection and away from various facilities.
  • Other factors affecting the running time include intersections, signal control, and pedestrian access.
  • Built environment, parking lot, gas station, etc. Intersections, schools, hospitals, clinics, and gas stations are selected as five types of influencing facilities, with the distance from each facility as a variable. In order to reflect the closer the distance from the facility, the greater the influence, the variable is taken as the decreasing function of the distance. Since the segment and the facility are far away to a certain extent, the impact of the facility can be ignored, and the segment with a distance greater than one kilometer is no longer affected.
  • the value of the distance variable for each segment within one kilometer is taken as 1-distance/1000, and the distance variable for each segment outside the one kilometer is taken as zero. Note that the processing for the intersection is to select the distance from the downstream intersection, and each road segment has only one downstream intersection. If the signal intersection and the unsignalized intersection or different intersection forms are regarded as parameters, any section The number of variables at each intersection of the segment should be less than or equal to 1.
  • the division period is 10 minutes, so the value of a set of variables is obtained every ten minutes.
  • the amount of floating car data obtained between 6 o'clock and 6:30 is small, and the difference between the estimated values of the trial run time is not Large, combine these three time periods into one time period.
  • the obtained parameter results are shown in the table.
  • the first 16 variables are equivalent to the running time (in s/m) of the road segment during the study period when it is away from the intersection and various facilities. Intersections, schools, hospitals, clinics, and gas station variables indicate increased operating time for each built environment when the distance is within one kilometer. The values of all variables are positive and there is a positive correlation between the running time of the road and the built environment.
  • Table 2 is the inverse of the logarithm of the maximum likelihood function value of the built-in environment explanatory variable (-LL)
  • the time obtained is basically in agreement with the “about 2.8 km/5 minutes” measured by Baidu map.
  • the gradual increase in travel time from six o'clock is also consistent with the actual situation.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

一种基于建成环境和低频浮动车数据的路段行程时间估计方法，属于城市交通管理及交通系统评价的技术领域。加入建成环境作为路段运行时间的解释变量，并通过算例证明了建成环境对于路段运行时间的解释性，给出了一种用路段上车辆数的分布情况估计路段上和路段间行程时间分配系数的方法，用于建立行程时间历史数据库后，代替距离作为路段运行时间分配系数。该方法解释了建成环境对路段运行时间的增加作用，并且能够反映路段不同部分运行速度之间的差异，提高路段行程时间估计结果的精度。

Description

基于建成环境和低频浮动车数据的路段行程时间估计方法 技术领域
本发明属于城市交通管理及交通系统评价的技术领域，涉及到ITS智能交通系统和ATIS出行者信息系统，特别涉及到建成环境对路段行程时间的解释及路段行程时间的估计方法。
背景技术
Liu H X利用浮动车数据结合传统线圈数据和信号灯相位信息提出了一种信号控制道路上行程时间预测的方法；Hellinga B研究了浮动车在两次报告间的运行时间如何分配到经过的相应的路段上，将每一个观测到的总行程时间分为自由流时间、控制停车延误、拥挤延误；Rahmani M等直接基于路径讨论运行时间的估计，并且提出了一种不用参数估计的行程时间估计方法，考虑与研究路径相重合的浮动车运行轨迹，认为在路径和浮动车轨迹上的运行速度保持一致，则路径及浮动车轨迹经过各路段花费的时间正比于在该路段上行驶的距离。
发明内容
本发明要解决的技术问题是用路段上车辆数的分布情况估计路段内和路段间行程时间分布的方法，用于建立行程时间历史数据库，可以代替距离作为路段运行时间分配系数。
本发明的技术方案：
基于建成环境和低频浮动车数据的路段行程时间估计方法，步骤如下：
(1)建立发送报告次数与运行时间的关系
在路段越拥堵、运行时间相对越长的路段上，浮动车发送报告的可能性越大，将浮动车发送报告这一事件作为随机变量，建立检测到的在各点浮动车发送报告次数与该点运行时间之间的关系。
浮动车发送报告的时间间隔是固定的，每个浮动车在任意时刻发送报告的可能性一致，设浮动车在每一时刻发送报告的概率均为ε，则
Figure PCTCN2017105633-appb-000001
其中，T为浮动车两次发送报告之间的时间间隔，ε为浮动车发送报告的频率；
在任意一点，浮动车在该点x汇报其位置的可能性ρx与该浮动车在该点x的运行时间成正比
Figure PCTCN2017105633-appb-000002
其中t(x)＜T
如果浮动车在某点处停留的时间大于u个发送报告周期，即t(x)＞uT，其中u∈N+且
Figure PCTCN2017105633-appb-000003
则u是最少发送报告的次数；其发送报告次数为u+1次的概率ρx为
Figure PCTCN2017105633-appb-000004
假设在研究的时间段内，交通状态不变，也就是各点的运行时间均不变；分别把每个点浮动车经过作为一随机事件，假设浮动车在这一交通状态不变的时间段内运行是无差异的，认为多个浮动车经过是独立重复试验，服从伯努利分布，
则当t(x)＜T时，在各点汇报其位置次数为nx的概率px为
Figure PCTCN2017105633-appb-000005
当t(x)＞uT,其中u∈N+时，在各点汇报其位置次数为nx的概率px为
Figure PCTCN2017105633-appb-000006
其中，0＜nx-mu＜m，即mu＜nx＜mu(+1)，这里假设在每一个小段车辆发送报告的次数最多差一次，考虑到使用的是低频浮动车数据，这一假设比较合理。
本小节根据浮动车在某点发送报告的可能性与在该点的运行时间成正比，建立了在某点检测到的发送报告的浮动车数与该点运行时间的关系。
(2)路段运行时间与交叉口及建成环境的关系
将路段划分为若干节段，而每一节段的运行时间取决于观测到和未观测到的节段属性，节段属性包括该节段距离下游交叉口的距离、距离人行横道的距离和该节段所属路段的属性(如车道宽度、车道数、几何线形等)。特别考虑了行人进出造成对路段上车辆的干扰或机动车进出而形成机动车间相互干扰特别大的建成环境对节段运行速度的影响。
用一个线性结构表示与节段的运行时间相关的解释变量(管制因素如道路等级、路段几何线性、附近的土地利用)和特定节段的长度对节段运行时间t'(x)的影响。即
Figure PCTCN2017105633-appb-000007
其中X表示路段，x表示其中某一节段，Aj表示影响节段运行时间的解释变量的值，例如道路等级、距离下游交叉口的距离等，αj表示各解释变量对节段运行时间的影响程度，为待估计的参数。
而得到路径运行时间的观测值为tok,
Figure PCTCN2017105633-appb-000008
k表示某一运行时间观测值，K表示所有运行时间观测值。各路段观测运行时间是其经过各节段的运行时间之和。而观测路段与节段的关系可以用一个K×X关联矩阵R表示，其中各个元素rkx表示各观测值k经过各节段x的距离与该节段总距离的比值。
Figure PCTCN2017105633-appb-000009
上面用线性组合的方式建立了路段运行时间与交叉口及建成环境之间的关 系。于是估计各个节段的运行时间就转化成了一个极大似然估计问题：
Figure PCTCN2017105633-appb-000010
其中αj为待估计的参数，m是估计的车辆总数，nx为发送报告的车辆数。估计的结果是各参数的值，而
Figure PCTCN2017105633-appb-000011
即可求出各个节段的运行时间。再根据路段和节段的关联矩阵即可求出路段的运行时间。
(3)路段行程时间的分配
路段内行程时间的分配：
在路段上总的运行时间是沿路段各点运行时间t"(x)的积分。即
Figure PCTCN2017105633-appb-000012
而路段内某一段运行时间是沿此段各点运行时间的积分，即
Figure PCTCN2017105633-appb-000013
得到的车辆数的期望等于在该点汇报其位置的概率p(x)与试验次数(即经过该点的总车辆数m)的乘积E(x)＝mp(x)。
而观测到的在该点汇报其位置的车辆数nx是期望的无偏估计。浮动车在该点的运行时间与浮动车在路段上各点汇报其位置的可能性成正比。所以，可以认为浮动车在该点的运行时间与浮动车在路段上各点汇报其位置的次数成正比。即t(x)∝p(x)∝E(x)∝nx。
可对路段进行分段，统计一定时间间隔内在各段期间车辆报告位置的总次数，则各段的运行时间与路段总运行时间的比值等于在这一段车辆发送报告的总次数与整条路段上车辆发送报告的总次数n(x)的比值。
Figure PCTCN2017105633-appb-000014
其中α1表示第一段的运行时间与路段总运行时间的比值，t1表示第一段的运 行时间，l1、l2表示第一、第二段的起点，L表示最后一段的终点。
路段间行程时间的分配：
在进行路段间出行时间的分配时，仍然沿用上述思路，认为在相同交通状态下，车辆通过两条或多条路段的任意位置是一独立重复试验。两个路段运行时间的比值根据同时经过这两个路段的车辆在这两个路段上发送报告的总次数之比得到:
Figure PCTCN2017105633-appb-000015
其中T1、T2分别表示两路段的运行时间，L1、L2分别表示两路段的长度，这样就得出了所有路段之间的比值，也就解决了路段间运行时间分配的问题。
本发明的有益效果：加入建成环境作为路段运行时间的解释变量，证明了建成环境对于路段运行时间的解释性；把交叉口运行时间包含到路段行程时间中，把与交叉口的距离作为路段行程时间的解释变量，能够有效考虑交叉口处交通管理与控制设施对运行时间的影响。还给出了一种用路段上车辆数的分布情况估计路段内和路段间行程时间分配系数的方法，用于建立行程时间历史数据库，作为路段运行时间分配系数，提高路段行程时间估计结果的精度。
具体实施方式
以下结合技术方案叙述本发明的具体实施方式，并模拟发明的实施效果。
基于建成环境和低频浮动车数据的路段行程时间估计方法，步骤如下：
1.不同时段影响路段运行时间的各变量对应的参数值
各个路段本身的设计等级、几何线性、车道数等对运行时间的影响设定为一个参数，相当于该路段在远离交叉口、远离各种设施时，在研究时段内的运行时间。其他影响运行时间的因素有交叉口、信号控制、行人进出量较大的路 边建成环境以及停车场、加油站等。选取交叉口、学校、医院、诊所、加油站作为五类影响设施，以各个节段距离设施的距离作为变量。为了体现距离设施的距离越近，影响越大这一特征，把变量取为距离的减函数。由于节段与设施远离到一定程度就可忽略该设施的影响，认为距离大于一公里的节段不再受影响。一公里范围内的各节段的距离变量的值取为1-distance/1000，而一公里范围外的各节段的距离变量取为0。注意，对于交叉口的处理是选取距离下游交叉口的距离，且每一个路段仅有一个下游交叉口，如果对于信号交叉口和无信号交叉口或者不同的交叉形式分别看作参数，任意一个节段的各个交叉口变量个数应小于等于1。
划分时段为10分钟，故每十分钟得到一组变量的值，六点钟到六点半之间得到的浮动车数据量较少，且试算的结果运行时间的估计值之间差别并不大，把这三个时段合并为一个时段。得到的参数结果如表所示。
表1出行时间各参数估计值
Figure PCTCN2017105633-appb-000016
Figure PCTCN2017105633-appb-000017
前16个变量相当于该路段在远离交叉口和各种设施时，在研究时段内的运行时间(单位s/m)。交叉口、学校、医院、诊所和加油站变量表示距离在一公里以内时，各建成环境增加的运行时间。所有变量的值都是正的，路段运行时间与建成环境之间是正相关的。
不加周围建成环境的解释变量时，其最大似然函数值与加周围建成环境的解释变量的极大似然函数值的对数的相反数对比如下图示所示。下表说明最小的似然比-2(LL-L0)＝30，而自由度为5，α＝0.05的χ2值为11.071，表明了把建成环境作为解释变量的合理性。
表2有无建成环境解释变量的最大似然函数值的对数的相反数(-LL)对比
Figure PCTCN2017105633-appb-000018
2.计算一条路径的运行时间
用所得参数计算沿锦山大街从丹东市公共交通总公司一公司到丹东市环境科学研究院的运行时间，结果如下表所示。同样体现了6：00-8：00之间增加的趋势。
表3沿锦山大街从丹东市公交一公司到环境科学研究院的运行时间随时间的变化
Figure PCTCN2017105633-appb-000019
将所得时间与百度地图所测得的“约2.8公里/5分钟”基本吻合。而从六点钟开始行程时间逐渐增加也与实际情况相符。

Claims (1)

  1. 一种基于建成环境和低频浮动车数据的路段行程时间估计方法，其特征在于，步骤如下：
    (1)建立发送报告次数与运行时间的关系
    将浮动车发送报告这一事件作为随机变量，建立检测到的在各点浮动车发送报告次数与该点运行时间之间的关系
    浮动车发送报告的时间间隔是固定的，每个浮动车在任意时刻发送报告的可能性一致，设浮动车在每一时刻发送报告的概率均为ε，则
    Figure PCTCN2017105633-appb-100001
    其中，T为浮动车两次发送报告之间的时间间隔，ε为浮动车发送报告的频率；
    在任意一点，浮动车在该点x汇报其位置的可能性ρx与该浮动车在该点x的运行时间成正比
    Figure PCTCN2017105633-appb-100002
    其中t(x)＜T
    如果浮动车在某点处停留的时间大于u个发送报告周期，即t(x)＞uT，其中u∈N+且
    Figure PCTCN2017105633-appb-100003
    则u是最少发送报告的次数；其发送报告次数为u+1次的概率ρx为
    Figure PCTCN2017105633-appb-100004
    假设在研究的时间段内，交通状态不变，也就是各点的运行时间均不变；分别把每个点浮动车经过作为一随机事件，假设浮动车在这一交通状态不变的时间段内运行是无差异的，认为多个浮动车经过是独立重复试验，服从伯努利分布，
    则当t(x)＜T在各点汇报其位置次数为nx的概率px为
    Figure PCTCN2017105633-appb-100005
    当t(x)＞uT,其中u∈N+时，在各点汇报其位置次数为nx的概率px为
    Figure PCTCN2017105633-appb-100006
    其中，0＜nx-mu＜m，即mu＜nx＜m(u+1)，假设在每一小段车辆发送报告的次数最多差一次；
    (2)路段运行时间与交叉口及建成环境的关系
    将路段划分为若干节段，每一节段的运行时间取决于观测到和未观测到的节段属性，节段属性包括该节段距离下游交叉口的距离、距离人行横道的距离和该节段所属路段的属性；用线性结构表示与节段的运行时间的解释变量和特定节段的长度对节段运行时间t'(x)的影响，即
    Figure PCTCN2017105633-appb-100007
    其中，X表示路段，x表示其中某一节段，Aj表示影响节段运行时间的解释变量的值，αj表示各解释变量对节段运行时间的影响程度，为待估计的参数；
    得到路径运行时间的观测值为
    Figure PCTCN2017105633-appb-100008
    k表示某一运行时间观测值，K表示所有运行时间观测值；各路段观测运行时间是其经过各节段的运行时间之和；观测路段与节段的关系用K×X关联矩阵R表示，其中各个元素rkx表示各观测值k经过各节段x的距离与该节段总距离的比值；
    Figure PCTCN2017105633-appb-100009
    于是估计各个节段的运行时间转化成一个极大似然估计问题：
    Figure PCTCN2017105633-appb-100010
    其中，αj为待估计的参数，m是估计的车辆总数，nx为发送报告的车辆数；
    Figure PCTCN2017105633-appb-100011
    即求出各个节段的运行时间，再根据路段和节段的关联矩阵即求出路段的运行时间；
    (3)路段行程时间的分配
    1)路段内行程时间的分配：
    在路段上总的运行时间是沿路段各点运行时间t"(x)的积分，即
    Figure PCTCN2017105633-appb-100012
    而路段内某一段运行时间是沿此段各点运行时间的积分，即
    Figure PCTCN2017105633-appb-100013
    得到的车辆数的期望等于在该点汇报其位置的概率p(x)与试验次数即经过该点的总车辆数m的乘积E(x)＝mp(x)；
    观测到的在该点汇报其位置的车辆数nx是期望的无偏估计，浮动车在该点的运行时间与浮动车在路段上各点汇报其位置的可能性成正比；所以，认为浮动车在该点的运行时间与浮动车在路段上各点汇报其位置的次数成正比，即t(x)∝p(x)∝E(x)∝nx；
    对路段进行分段，统计一定时间间隔内在各段期间车辆报告位置的总次数，则各段的运行时间与路段总运行时间的比值等于在这一段车辆发送报告的总次数与整条路段上车辆发送报告的总次数n(x)的比值；
    Figure PCTCN2017105633-appb-100014
    其中，α1表示第一段的运行时间与路段总运行时间的比值，t1第一段的运行时间，l1、l2表示第一、第二段的起点，L表示最后一段的终点；
    2)路段间行程时间的分配：
    在进行路段间出行时间的分配时，仍然沿用路段内行程时间的分配思路，认为在一相同交通状态下，多个车辆连续通过两条或多条路段的任意位置是一独立重复试验；两个路段运行时间的比值根据同时经过这两个路段的车辆在这两个路段上发送报告的总次数之比得到
    Figure PCTCN2017105633-appb-100015
    其中T1、T2分别表示两路段的运行时间，L1、L2分别表示两路段的长度,即得出所有路段之间的比值，也就得出了路段间运行时间的分配。
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