JP2523800B2 - Traffic jam situation message automatic editing method - Google Patents
Traffic jam situation message automatic editing methodInfo
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- JP2523800B2 JP2523800B2 JP63169448A JP16944888A JP2523800B2 JP 2523800 B2 JP2523800 B2 JP 2523800B2 JP 63169448 A JP63169448 A JP 63169448A JP 16944888 A JP16944888 A JP 16944888A JP 2523800 B2 JP2523800 B2 JP 2523800B2
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Description
【発明の詳細な説明】 産業上の利用分野 本発明は、道路の交通状況を的確に検出し、きめ細か
く、タイミングよく交通情報を自動提供するための交通
情報提供システムにおける路側通信等に使用する渋滞状
況メッセージ自動編集方法に関する。The present invention relates to a traffic jam used for roadside communication or the like in a traffic information providing system for accurately detecting a traffic situation on a road and automatically providing detailed and timely traffic information. A method for automatically editing a status message.
従来の技術 従来、交通状況の検出は車両感知器ごとの速度あるい
は占有率に対してある基準値を与え、得られた値との大
・小を比較することにより渋滞・非渋滞を判定し、渋滞
長を算出していた。また、渋滞区間における真の渋滞長
と補正された渋滞長との比率から渋滞区間長比率を算出
し、渋滞長と渋滞区間長比率の組み合わせにより、判定
値にしたがってあらかじめ設定された渋滞状況表現を選
択していた。Conventional technology Conventionally, the detection of traffic conditions gives a certain reference value to the speed or occupancy rate of each vehicle detector, and judges the congestion / non-congestion by comparing the obtained value with the magnitude. I was calculating the congestion length. In addition, the congestion section length ratio is calculated from the ratio of the true congestion length and the corrected congestion length in the congestion section, and the combination of the congestion length and the congestion section length ratio provides a congestion status expression preset according to the judgment value. Had been selected.
発明が解決しようとする課題 しかしながら、従来方法では渋滞・非渋滞のように曖
昧な状態を分類する際、ある基準値をもとに行っている
ため、基準値付近の値に対しては決定的な判定が難し
く、また、渋滞状況を表現する場合についても渋滞表現
自体が曖昧であり、さまざまな交通状況が重なり合い、
さらに運用者によっても表現が左右されるため、数値を
用いた確定的な判定値による決定では困難であった。However, the conventional method is based on a certain reference value when classifying an ambiguous state such as congestion or non-congestion, and therefore is decisive for values near the reference value. It is difficult to judge, and when expressing the traffic jam situation, the traffic jam expression itself is ambiguous, and various traffic situations overlap.
Furthermore, because the expression also depends on the operator, it was difficult to make a decision based on a definite judgment value using numerical values.
本発明は、このような従来の問題を解決するものであ
り、主観・経験・曖昧さなど人間のもつ特性を考慮し、
交通情報メッセージ作成に関し、システム運用者の意図
を反映することを実現する優れた渋滞状況メッセージ自
動編集方法を提供することを目的としたものである。The present invention solves such a conventional problem, considering characteristics of human beings such as subjectivity, experience, and ambiguity,
It is an object of the present invention to provide an excellent method for automatically editing a traffic jam condition message that realizes reflecting the intention of a system operator in creating a traffic information message.
課題を解決するための手段 本発明は、上記目的を達成するために、主観的な、か
つ曖昧な量を扱うことが可能なファジィ理論を導入し、
経験則・主観的要因の強い渋滞状況分類および渋滞状況
表現決定に対し、状態の度合いを数値化、ルール化する
ようにしたものである。Means for Solving the Problems In order to achieve the above object, the present invention introduces a fuzzy theory capable of handling a subjective and ambiguous amount,
This is a method to quantify and rule the degree of the state for the congestion situation classification and the determination of the congestion situation expression with strong rules of thumb and subjective factors.
作 用 したがって、本発明によれば、交通情報提供メッセー
ジ作成に対し、上記方法を導入することにより、従来の
ような確定的な硬いシステム運用から人間の特性を考慮
した柔らかなシステム運用の実現が可能になり、コンピ
ュータがあたかも経験豊かな熟練運用者のように制御す
るという効果を有することになる。Therefore, according to the present invention, by introducing the above method for creating a traffic information providing message, it is possible to realize a soft system operation considering human characteristics from the conventional deterministic and hard system operation. It will be possible and will have the effect of controlling the computer as if it were an experienced and experienced operator.
実施例 第１図は路線上の各交差点間（事象点とよぶ）におけ
る車両感知器設置例であり、１事象点に対して複数個の
車両感知器設置点が存在する。各車両感知器設置地点に
は１個、または複数個の車両感知器が設置される。第１
図中の「▲」は車両感知器を、また事象点の上流側の交
差点をダミーの車両感知器設置地点とし、「△」で表
す。Example FIG. 1 shows an example of installing vehicle detectors between intersections on a route (called event points), and there are a plurality of vehicle detector installation points for one event point. One or a plurality of vehicle detectors are installed at each vehicle detector installation point. First
In the figure, “▲” represents a vehicle detector, and the intersection on the upstream side of the event point is a dummy vehicle detector installation point, and is represented by “Δ”.
第２図は道路における渋滞状況の状態分類および渋滞
指標を示す。渋滞状況を「渋滞」「やや渋滞」「非渋
滞」の３分類し、渋滞指標は区間〔0,1〕で表現する。
第３図・第４図は３分類した渋滞状況の各状態分類に対
するメンバシップ関数の１例である。本例では、直線に
よるメンバシップ関数の場合を示した。第５図は車両感
知器から占有率が得られた場合の各分類における渋滞指
標である。以上、第２図から第５図までが各車両感知器
設置地点における渋滞状況の分類・渋滞指標算出につい
て示した図であり、この処理過程を次に述べる。FIG. 2 shows the state classification of the traffic jam situation on the road and the traffic jam index. Congestion status is classified into "congestion", "moderate congestion" and "non-congestion", and the congestion index is expressed by the section [0, 1].
FIG. 3 and FIG. 4 are examples of membership functions for each state classification of the three classified congestion situations. In this example, the case of a membership function based on a straight line is shown. FIG. 5 is a congestion index in each classification when the occupancy rate is obtained from the vehicle detector. As described above, FIG. 2 to FIG. 5 are diagrams showing the classification of the traffic jam situation and the calculation of the traffic jam index at each vehicle detector installation point, and the processing steps will be described below.
感知器設置地点ｉに対し、車両感知器ｊ（ｊ＝1,2,
…）の占有率を▲ｔi j▼（０≦▲ｔi j▼≦100）とす
る。地点ｉにおける占有率は下式で表す。The vehicle detector j (j = 1, 2,
,) Is set to ▲ t i j ▼ (0 ≦ ▲ t i j ▼ ≦ 100). The occupation rate at the point i is expressed by the following formula.
次に、tiを用い、あらかじめ用意した渋滞状況分類別
のメンバシップ関数によって渋滞指標を算出する。渋滞
状況は前に述べたように３分類されており、各メンバシ
ップ関数は車両感知器設置地点ごとに、第３図・第４図
に示すようなTLi（ti）、TMi（ti）、TSi（ti）で定義
する。車両感知器設置地点ｉから得られたtiに対して各
々 を求め、ziを下式にもとづき算出する。 Next, using t i , a congestion index is calculated using a membership function prepared in advance for each category of traffic congestion. The traffic congestion status is classified into three categories as described above, and the membership functions are TLi (t i ) and TMi (t i ) as shown in Figs. 3 and 4 for each vehicle detector installation point. , TSi (t i ). For each t i obtained from the vehicle detector installation point i And z i is calculated based on the following formula.
ここで であり、ziを感知器設置地点ｉにおける渋滞指標と定義
する。ziの値は、０≦zi≦１で示され、値が大きいほど
渋滞程度が高いことを意味する。渋滞・非渋滞かを判別
するにはある定数αｉ（０≦αｉ≦１）に対し、 zi≧αｉならば渋滞 zi＜αｉならば非渋滞 とする。 here And z i is defined as the congestion index at the sensor installation point i. The value of z i is represented by 0 ≦ z i ≦ 1, and the larger the value, the higher the degree of traffic congestion. To determine whether it is a traffic jam or a non-traffic jam, a certain constant α i (0 ≦ α i ≦ 1) is set. If z i ≧ α i , the traffic jam is z i <α i .
第６図は各感知器設置地点で渋滞判定した結果をもと
に、路線上での渋滞地点の連結方法を示した図である。
図中で「１」は渋滞地点、「０」は非渋滞地点を表す。
路線上で「渋滞」と判定された感知器設置地点に隣接し
た感知器設置地点が「渋滞」と判定されている場合、双
方の地点間距離が一定値β以下であれば、２つの地点を
連結させる。この操作を繰り返して「渋滞区間」をつく
る。事象点の上流地点は感知器設置地点のダミーとして
「渋滞」と設定しておく。なお、事象点間に車両感知器
が存在しない場合、事象点同士の連結は行わない。FIG. 6 is a diagram showing a method of connecting traffic congestion points on a route based on the result of traffic congestion determination at each sensor installation point.
In the figure, "1" represents a congestion point and "0" represents a non-congestion point.
If the sensor installation point adjacent to the sensor installation point determined to be "congestion" on the route is determined to be "congestion", if the distance between the two points is less than a certain value β To connect. Repeat this operation to create a "congestion zone". The upstream point of the event point is set as "congestion" as a dummy of the sensor installation point. If there is no vehicle detector between the event points, the event points are not connected to each other.
第７図は渋滞区間の補正方法を示したものである。す
なわち、路線上で渋滞区間１と渋滞区間３との間に非渋
滞区間２が存在する場合、下記の条件を満たせば、非渋
滞区間２を「渋滞区間」と判定し、連続した渋滞区間と
みなす。渋滞区間１、３を「真の渋滞小区間」、渋滞区
間２を「補正渋滞小区間」、各渋滞小区間を連結してで
きた区間を改めて渋滞区間とよぶ。FIG. 7 shows a method of correcting a traffic jam section. That is, when the non-congestion section 2 exists between the congestion section 1 and the congestion section 3 on the route, if the following conditions are satisfied, the non-congestion section 2 is determined to be a "congestion section", and the continuous congestion section is determined. I reckon. Congestion sections 1 and 3 are referred to as "true congestion subsections", congestion section 2 is referred to as "correction congestion subsections", and sections formed by connecting each congestion subsection are referred to as "congestion sections".
（１） y2＜ζ （２） y2＜x1かつy2＜x3 ただし、x1、x3、y2はそれぞれ小区間１、小区間３、
小区間２の区間長とし、ζは判定基準値とする。(1) y 2 <ζ (2) y 2 <x 1 and y 2 <x 3 where x 1 , x 3 and y 2 are sub-section 1, sub-section 3, and
The section length of the small section 2 is set, and ζ is set as the determination reference value.
第８図は、真の渋滞小区間と補正渋滞小区間とによっ
て渋滞区間長比率の算出方法を表した図である。渋滞区
間長比率ｒを次の式で定義する。FIG. 8 is a diagram showing a method of calculating the congestion section length ratio based on the true congestion small section and the corrected congestion small section. The congestion section length ratio r is defined by the following formula.
ただし、ｋは渋滞区間、x1、x2、x3は真の渋滞小区間
長、y1、y2は補正渋滞小区間長、ｌは小区間を意味す
る。この場合、 が渋滞区間ｋの渋滞長となり、dkで表す。 However, k is a congestion section, x 1 , x 2 , x 3 are true congestion small section lengths, y 1 and y 2 are corrected congestion small section lengths, and 1 is a small section. in this case, Is the congestion length of the congestion section k and is represented by d k .
次に、渋滞区間ｋにおける各車両感知器設置地点ｉの
渋滞指標ziを用い、渋滞区間ｋの渋滞指標skを で表す。ここで、wiは車両感知器設置地点ｉに与えられ
た重み係数であり、 である。Next, the congestion index s k of the congestion section k is used by using the congestion index z i of each vehicle detector installation point i in the congestion section k. It is represented by. Where w i is a weighting factor given to the vehicle detector installation point i, Is.
第９図・第10図・第11図は渋滞区間の表現方法を説明
した図である。渋滞区間の表現方法として「〜付近を先
頭に〜付近まで」「〜付近で」の２種類用意し、各感知
器設置地点の所属事象点をあらかじめ設定しておく。次
に、渋滞区間の最下流感知器設置地点を渋滞区間先頭地
点、最上流感知器設置地点を渋滞区間末尾地点とし、各
々の感知器設置地点がいずれの事象点に属しているかを
求めることにより渋滞区間の先頭地点・末尾地点を決定
する。第９図は「m1付近を先頭にm2付近まで」、また第
10図・第11図は「m3付近で」と表現した例である。FIG. 9, FIG. 10, and FIG. 11 are diagrams for explaining a method of expressing a traffic jam section. As a method of expressing a traffic jam section, two types are prepared: "from the vicinity to the beginning to the vicinity" and "at the vicinity", and the event points to which each sensor is installed are set in advance. Next, by setting the most downstream sensor installation point of the congestion section as the congestion section start point and the most upstream sensor installation point as the congestion section end point, by determining which event point each sensor installation point belongs to Determine the start and end points of the traffic jam section. Fig. 9 shows "From near m 1 to near m 2 "
10 figures and FIG. 11 shows an example in which the expression "in the vicinity of m 3".
第12図は各渋滞状況の適合度を説明した図である。先
に求めた渋滞区間長dk・渋滞指標sk・渋滞区間長比率rk
を用い、渋滞長は「渋滞が長い・中くらい・短い」、渋
滞指標は「高い・中くらい・低い」、渋滞長区間比率は
「高い・中くらい・低い」というように、各々の状況を
３つの状態に分類する。また、各々の状況分類に対して
メンバシップ関数を与え、入力値dk・sk・rkにしたがっ
て適合度を決定する。各状況は以下の式で決定する。FIG. 12 is a diagram explaining the suitability of each traffic jam situation. Congestion section length d k obtained earlier, congestion index s k , congestion section length ratio r k
The congestion length is “long / medium / short”, the congestion index is “high / medium / low”, and the congestion length section ratio is “high / medium / low”. Classify into three states. Also, a membership function is given to each situation classification, and the goodness of fit is determined according to the input values d k · s k · r k . Each situation is determined by the following formula.
p1＝max（DL（dk）,DM（dk）,DS（dk）） p2＝max（SL（sk）,SM（sk）,SS（sk）） p3＝max（RL（rk）,RM（rk）,RS（rk）） ここでDL（dk）・DM（dk）・DS（dk）、SL（sk）・SM
（sk）・SS（sk）、RL（rk）RM（rk）・RS（rk）は、そ
れぞれ渋滞長、渋滞指標、渋滞区間長比率の大・中・小
に対する適合度である。この適合度の最大値p1、p2、p3
を達成する状態分類を、その状況を代表とする状態とす
る。p 1 = max (DL (d k ), DM (d k ), DS (d k )) p 2 = max (SL (s k ), SM (s k ), SS (s k )) p 3 = max (RL (r k ), RM (r k ), RS (r k )) where DL (d k ) · DM (d k ) · DS (d k ), SL (s k ) · SM
(S k ) / SS (s k ), RL (r k ) RM (r k ), and RS (r k ) are the conformity of the congestion length, congestion index, and congestion interval length ratio to large / medium / small, respectively. is there. The maximum values of this goodness of fit p 1 , p 2 , p 3
The state classification that achieves is the state that represents the situation.
第13図・第14図は渋滞長の状態分類に対するメンバシ
ップ関数の１例、第15図・第16図は渋滞指標の状態分類
に対するメンバシップ関数の１例、第17図・第18図は渋
滞区間長比率の状態分類に対するメンバシップ関数の１
例である。本例では、直線によるメンバシップ関数の場
合を示した。Figures 13 and 14 are examples of membership functions for state classification of congestion length, Figures 15 and 16 are examples of membership functions for state classification of congestion indicators, and Figures 17 and 18 are Membership function 1 for state classification of congestion section length ratio
Here is an example. In this example, the case of a membership function based on a straight line is shown.
次に、第19図に示した例のように渋滞長・渋滞指標・
渋滞区間長比率の各状況分類の組み合わせにしたがって
ルールを選択し、渋滞状況の表現を決定する。表現とし
て「ぎっしり渋滞しています」「渋滞しています」「断
続的に渋滞しています」「流れが悪くなっています」を
用意する。ルールの記述は例えば、 「もし、渋滞が長く、かつ渋滞指標が中くらいで、かつ
渋滞区間長比率が低ければ、 の場合、渋滞状況を『断続的に渋滞しています』、 の場合、渋滞状況を『流れが悪くなっています』 と表現する。」 ということを意味する。ルールのなかでμは μ＝min（p1,p2,p3） により算出し、ｈはルール、 はルールｈにおける基準値を表す。Next, as in the example shown in Fig. 19, congestion length / congestion index /
A rule is selected according to the combination of each situation classification of the congestion section length ratio, and the expression of the congestion situation is determined. As expressions, we will prepare "congested traffic", "congested traffic", "intermittent traffic congestion", and "flow is getting worse". The description of the rule is, for example, "If the traffic jam is long, the traffic congestion index is medium, and the traffic congestion section length ratio is low, In the case of, the traffic condition is "intermittently congested", In the case of, the congestion situation is expressed as “the flow is getting worse”. It means that. In the rule, μ is calculated by μ = min (p 1 , p 2 , p 3 ), h is the rule, Represents the reference value in rule h.
第20図は渋滞長の表現を示した図である。渋滞長はキ
ロ（km）単位とし、「キロ以上」と表現する。ただし、
dk＜1kmの場合、および「流れが悪くなっています」と
表現された場合は渋滞長の表現を行わない。FIG. 20 is a diagram showing an expression of the length of traffic jam. Congestion length is in units of kilometers (km) and is expressed as "more than kilometers". However,
If d k <1km, or if "the flow is getting worse", the congestion length is not expressed.
第21図は最終的な渋滞状況基本メッセージパタンであ
る。「 」の部分は語句が挿入され、「 」の部分は省
略可能な語句である。第22図は具体的な渋滞状況メッセ
ージ作成例である。Figure 21 is the final basic message pattern for traffic congestion. A phrase is inserted in the part of "", and an optional phrase is included in the part of "". FIG. 22 shows a concrete example of creating a traffic jam situation message.
以上のように、コンピュータに本発明を組み込むこと
によって自動的に渋滞状況メッセージを編集することが
できる。As described above, by incorporating the present invention in a computer, it is possible to automatically edit a traffic jam status message.
発明の効果 本発明は上記実施例より明らかなように、以下に示す
効果を有する。EFFECTS OF THE INVENTION The present invention has the following effects, as is apparent from the above-described embodiments.
（１） 車両感知器の設置地点毎に渋滞の有無の判別か
ら渋滞小区間の長さ及びこの渋滞小区間の渋滞指標を算
出し、この各パラメータに対して複数の状態に分類し、
この各状態に対してメンバシップ関数を与え、算出され
た渋滞小区間の長さ及びこの渋滞小区間の渋滞指標値の
値の適合から、各パラメータの状態を決定し、この決定
された各パラメータの状態の組合せで、渋滞状況の表現
を決定して渋滞状況メッセージを作成することにより、
渋滞区間を抽出するとともに、この渋滞区間に対する渋
滞状況を決定することができるため、より広い範囲で複
数の交通情報を考慮したメッセージを作成することがで
きる。(1) Calculate the length of the small congestion section and the congestion index of this small congestion section from the presence or absence of congestion at each vehicle detector installation point, and classify into multiple states for each parameter,
A membership function is given to each state, the state of each parameter is determined from the calculated length of the congestion small section and the value of the congestion index value of this congestion small section, and each determined parameter is determined. By deciding the expression of the traffic jam situation with the combination of the states of and creating a traffic jam situation message,
Since the traffic jam section can be extracted and the traffic jam condition for the traffic jam section can be determined, it is possible to create a message considering a plurality of traffic information in a wider range.
（２） 隣合う２つの渋滞小区間に対し、この２つの渋
滞小区間の間の区間が、予め設定された基準値より小さ
く、かつ、隣接する渋滞小区間の長さに対して小さい
と、この区間及びこの区間に隣接する渋滞小区間とを連
結し、この連結した区間を渋滞区間とし、各々の渋滞小
区間の長さを加えた値に対し、渋滞区間の長さで割って
算出した比率を用いることにより、「ぎっしり渋滞」な
のか、それとも「断続的に渋滞」なのかなど、渋滞区間
内の渋滞状況をより細かい表現で渋滞状況メッセージを
作成することができる。(2) For two adjacent small congestion sections, if the section between the two small congestion sections is smaller than a preset reference value and smaller than the length of the adjacent small congestion sections, This section and the congested small section adjacent to this section were connected, this connected section was defined as the congested section, and the value obtained by adding the length of each congested small section was divided by the length of the congested section. By using the ratio, it is possible to create a traffic jam status message with a more detailed expression of the traffic jam status within the traffic jam section, such as "congested traffic jam" or "intermittent traffic jam".
第１図は路線上の各事象点間における車両感知器設置地
点例、第２図は渋滞状況の状態分類および渋滞指標例、
第３図・第４図はメンバシップ関数の１例、第５図は占
有率からの渋滞指標算出方法、第６図は渋滞地点連結方
法、第７図は渋滞区間補正方法、第８図は渋滞区間長比
率の算出方法、第９図・第10図・第11図は渋滞区間の表
現方法、第12図は渋滞状況適合度の説明、第13図・第14
図は渋滞長の状態分類に対するメンバシップ関数の１
例、第15図・第16図は渋滞指標の状態分類に対するメン
バシップ関数の１例、第17図・第18図は渋滞区間長比率
の状態分類に対するメンバシップ関数の１例、第19図は
渋滞状況表現に対するルールの１例、第20図は渋滞長表
現方法である。第21図は渋滞状況基本メッセージパタ
ン、第22図は具体的な渋滞状況メッセージ例である。Fig. 1 is an example of a vehicle detector installation point between event points on the route, Fig. 2 is an example of traffic condition classification and congestion index examples,
3 and 4 are examples of membership functions, FIG. 5 is a method for calculating congestion index from occupancy, FIG. 6 is a method for connecting congestion points, FIG. 7 is a method for correcting congestion sections, and FIG. Congestion section length ratio calculation method, Figure 9, Figure 10, Figure 11 is a method of expressing congestion section, Figure 12 is an explanation of congestion situation suitability, Figure 13, Figure 14
The figure shows one of the membership functions for classifying congestion length states.
Examples, Fig.15 and Fig.16 are examples of membership functions for state classification of congestion indicators, Fig.17 and Fig.18 are examples of membership functions for state classification of congestion interval length ratio, and Fig.19 is Fig. 20 shows an example of rules for expressing traffic jam status, which is a method for expressing traffic jam length. FIG. 21 is a basic message pattern of traffic jam status, and FIG. 22 is an example of a concrete message of traffic jam status.
Claims (2)
を算出し、この占有率を一つの変数としたメンバシップ
関数から渋滞状況を示す渋滞指標を算出し、この渋滞指
標が予め設定された値より大きいと上記車両感知器の設
置地点は渋滞であると判別し、隣接する上記車両感知器
の設置地点が双方とも渋滞であると判別し、かつ、この
双方の車両感知器の設置地点間の距離が一定値以下であ
ると上記２つの車両感知器の設置地点間を渋滞小区間と
し、上記車両感知器の設置地点毎に算出した上記渋滞指
標から上記渋滞小区間の渋滞指標を算出し、上記渋滞小
区間の長さ、この渋滞小区間の渋滞指標のパラメータに
対し、複数の状態に分類し、この分類された状態毎にメ
ンバシップ関数を与え、この状態毎の各々のメンバシッ
プ関数に対し、上記算出された上記渋滞小区間の長さの
値及びこの渋滞区間の渋滞指標の値における適合度を決
定し、上記パラメータ毎に決定した状態に対し、この各
々の状態の組合せから渋滞状況の表現を決定し、渋滞状
況メッセージを作成する渋滞状況メッセージ自動編集方
法。1. A occupancy rate is calculated from a vehicle detector installed on a road, and a congestion index indicating a congestion status is calculated from a membership function using the occupancy rate as one variable, and the congestion index is preset. If it is larger than the specified value, it is determined that the vehicle detector installation point is in a traffic jam, both adjacent vehicle sensor installation points are in a traffic jam, and both vehicle detectors are installed. If the distance between the points is a certain value or less, the two vehicle detector installation points are set as the traffic congestion small section, and the traffic congestion index of the traffic congestion small section is calculated from the traffic congestion index calculated for each vehicle sensor installation point. Calculate and classify into a plurality of states for the length of the above-mentioned congestion small section and the parameter of the congestion index of this congestion small section, give a membership function for each of the classified states, and give each member for each state. Above the ship function The calculated value of the length of the congestion small section and the degree of conformity in the value of the congestion index of this congestion section are determined, and for the states determined for each of the above parameters, the expression of the congestion state is expressed from the combination of each state. Congestion status message automatic editing method to decide and create congestion status message.
の渋滞小区間の間の区間が、予め設定された基準値より
小さく、かつ、隣接する上記渋滞小区間の長さに対して
小さいとこの区間及びこの区間に隣接する渋滞小区間と
を連結し、この連結した区間を渋滞区間とし、上記各々
の渋滞小区間の長さを加えた値に対し、上記渋滞区間の
長さで割って算出した比率を渋滞区間長比率とし、車両
感知器の設置地点毎に算出した渋滞指標から、上記渋滞
区間の渋滞指標を算出し、上記渋滞区間の長さ、この渋
滞区間の渋滞指標及び上記渋滞区間長比率の３つのパラ
メータから渋滞状況の表現を決定し、渋滞状況メッセー
ジを作成することを特徴とする請求項（１）記載の渋滞
状況メッセージ自動編集方法。2. For two adjacent traffic congestion small sections, a section between the two traffic congestion small sections is smaller than a preset reference value and the length of the adjacent traffic congestion small section is smaller than a reference value set in advance. If it is small, this section and the small congestion section adjacent to this section are connected, this connected section is defined as the congestion section, and the length of each congestion section is added to the value obtained by adding the length of each congestion small section. The ratio calculated by dividing is the congestion section length ratio, and the congestion index of the above congestion section is calculated from the congestion index calculated for each installation location of the vehicle detector, and the length of the above congestion section, the congestion index of this congestion section and The traffic jam condition message automatic editing method according to claim 1, wherein the expression of the traffic jam condition is determined from three parameters of the traffic jam section length ratio and a traffic jam condition message is created.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63169448A JP2523800B2 (en) | 1988-07-07 | 1988-07-07 | Traffic jam situation message automatic editing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP63169448A JP2523800B2 (en) | 1988-07-07 | 1988-07-07 | Traffic jam situation message automatic editing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH0218699A JPH0218699A (en) | 1990-01-22 |
| JP2523800B2 true JP2523800B2 (en) | 1996-08-14 |
Family
ID=15886790
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP63169448A Expired - Fee Related JP2523800B2 (en) | 1988-07-07 | 1988-07-07 | Traffic jam situation message automatic editing method |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP2523800B2 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102592447A (en) * | 2011-12-20 | 2012-07-18 | 浙江工业大学 | Method for judging road traffic state of regional road network based on fuzzy c means (FCM) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2784928B2 (en) * | 1988-11-18 | 1998-08-13 | マツダ株式会社 | Engine control device |
| JPH064795A (en) * | 1992-06-17 | 1994-01-14 | Hitachi Ltd | Device and method for monitoring traffic state and traffic flow monitoring control system |
| KR100536322B1 (en) * | 2000-11-10 | 2005-12-12 | 에스케이 주식회사 | Traffic Information Transmission Method and its System |
| JP2007263973A (en) * | 2007-06-04 | 2007-10-11 | Toyota Motor Corp | Traffic information output device and method |
| JP5024879B2 (en) * | 2007-11-30 | 2012-09-12 | トヨタ自動車株式会社 | Traffic information creation method and traffic information creation device |
| JP5513361B2 (en) * | 2010-12-24 | 2014-06-04 | 株式会社ゼンリンデータコム | Traffic jam information generating apparatus, traffic jam information generating method, and program |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS59204707A (en) * | 1983-05-09 | 1984-11-20 | Hitachi Ltd | Estimating method of state estimating system |
| JPS62241002A (en) * | 1986-04-11 | 1987-10-21 | Mitsubishi Electric Corp | Auto-tuning controller |
| JPH083880B2 (en) * | 1986-06-13 | 1996-01-17 | 松下電器産業株式会社 | Road traffic information monitoring device |
| JP2529215B2 (en) * | 1986-08-22 | 1996-08-28 | 松下電器産業株式会社 | Congestion traffic flow analyzer |
| JPS63123102A (en) * | 1986-11-12 | 1988-05-26 | Hitachi Ltd | Digital control system for fuzzy inference |
-
1988
- 1988-07-07 JP JP63169448A patent/JP2523800B2/en not_active Expired - Fee Related
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102592447A (en) * | 2011-12-20 | 2012-07-18 | 浙江工业大学 | Method for judging road traffic state of regional road network based on fuzzy c means (FCM) |
| CN102592447B (en) * | 2011-12-20 | 2014-01-29 | 浙江工业大学 | Method for judging road traffic state of regional road network based on fuzzy c means (FCM) |
Also Published As
| Publication number | Publication date |
|---|---|
| JPH0218699A (en) | 1990-01-22 |
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