On the layout of fixed urban traffic detectors: an application study

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE • 6 • SUMMER 2009 1939-1390/09/$26.00©2009IEEE

Digital Object Identifier 10.1109/MITS.2009.933858

Abstract — One of the basic functions of Intelligent Trans-portation System is to collect traffic data through detectors. Thus, the layout of traffic detectors on the urban road system is important for the development of Intelligent Transportation System. With the urgent demand for real-time traffic infor-mation, a high-density layout of detectors has been adopted resulting in huge investment. Based on the real demand for the construction of an urban traffic flow detector system, a set of principles for the layout of traffic detectors are proposed, which could be easily carried out to determine the densities and positions of the detectors in practical applications. An ap-plicant example of these principles is also given to explain the whole process in detail, including the method to choose the areas, roads, links and positions for detector installation.

Index Terms — Intelligent Transportation System, Traffic flow detector, Layout.

Runmei Li School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, P.R. China, [email protected]

Libin JiaBeijing Weize Engineering & Project Management, Beijing, P.R. China [email protected]

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE • 7 • SUMMER 2009

I. Introduction

Intelligent Transportation System (ITS) is considered the best solution for improving the utility of transportation resources. An important function of ITS is information collection, which

is performed by traffic flow detectors. Whether the data collected by traffic detectors indeed accurately reflect the real-time traffic flow status depends to a large extent on the distribution and density of detectors, and to supply adequate information the density of detectors should be high enough to adequately cover the entire traffic network. This is particularly true for fixed detectors such as loop detectors, microwave detectors, video detectors, etc. Considering the large size of modern cities, it is easy to see conflicts arising between the detector density requirement and the budget limit.

However, a major observation is that if we install the detectors rationally and efficiently, the number of detectors can be significantly reduced. It is thus important to find methods to work out a rational layout of traffic detectors that meets the require-ments from ITS while requires reasonable invest-ment. Such a study is important not only in its own right, but also for the overall quality of ITS.

II. Review of Methods for Urban Traffic Detector LayoutThere are many theoretical methods for fixed traffic flow detector layout, which can be classified into three categories: methods based on travel time estimation, methods based on traffic flow volume estimation, and methods based on Origin-Destination (OD) traffic demand estimation. For the first type of methods, a two-level programming model is proposed with travel time information by Chan and Lam [1]. Jiang [2] studied the optimal space distribution of detectors with statistic analysis techniques. The second type methods include the gray cluster analysis method [3], data mining [4], and digraph theory [5]. The Markov decision method [6] belongs to the third kind.

However, almost all of the previous methods require large numbers of real-time traffic flow data to analyze traffic status in every link of net-work, and then to decide how to design detectors layout. The characters of network, road and links and investment scale of detectors are not consid-ered or considered in a small extent. The limitation of these methods or theories manifests itself more when they were applied to practice, especially for the problem of how to get abundant, more accurate real traffic flow data. On the other hand, with the development of the urban transportation system and technology, the distribution and density of traf-fic flow detector increased. It is virtually impos-sible for a city to construct once for all a detector ©

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system that will meet the needs of traffic managers and traffic users forever. So, when installing new detectors in urban traffic network, the detectors installed before must be taken into consideration.

Now, a widely used way is fix-distance method with the goal of getting the great possible exact and adequate traffic flow data. In Hong Kong, the Transport Department has investigated the feasibility of implementing a driver information system covering the Tuen Mun road, and the detectors are recommended with density of two detectors/km. In Japan, the detectors are installed in each lane of the Meishin and the Tomei expressways at intervals of about 2 km. In American Chicago, 1700 detectors distribute on 355 kilometers expressway, five detectors/km; And in Can-ada Toronto, the distance between two detectors is 600~700 meters [1].

The method of high density fix-distance can easily be applied to practice and the precision of traffic information proposed to traffic managers can be guaranteed, while the amount of investment has been sharply increased. At the same time, such a large scale construction of detector sys-tem may affect the traffic flow seriously. Thus, the method of fix-distance is often used in some especially important roads such as expressways or arterial roads.

Now, researchers face two problems, one is they don’t have enough data to use theoretical analysis methods described in [1–5]; the other is they don’t have enough money to deploy detectors according to the high density fix-distance method. Determining the optimal detector layout which can minimize both the investment cost and the traffic flow measurement error remains challenge in practice.

In the paper, the problems given above are discussed. Based on the real demands of real-time traffic information in ITS, a set of principles and methods are proposed. These principles and methods could be used to determine detec-tor density and position in practical application. A real ex-ample is used to illustrate the whole process of choosing areas, roads, links and positions for detector installation using these principles and methods.

III. The Principles of Detector Layout

A. Economic PrincipleEconomic principle means when determining the optimal de-tector layout the investment cost should be considered firstly.

Based on the previous experience of constructing de-tectors system, the cost of installing one detector can be estimated. Then given investment budget, the number of detectors can be almost exactly determined. On the other hand, based on the economic principle, we will make the best use of the circumstance when to determine the instal-lation position of detector so that to reduce the cost. For example, an overpass already built can be used to install a video detector.

B. Prior PrinciplePrior principle includes four items:

The important area has top priority;1) The important road has top priority; 2) The new road has the second priority;3) The congestion road has the third priority.4) It is impossible for a city to construct once for all a de-

tector system that will meet the needs of ITS for ever. So when installing new detectors in urban traffic network, researchers should know firstly which areas and which roads are important according to urban development state and future development plan. Such as economic de-velopment zone is an important area in which traffic flow information is urgent demanded by transportation man-agers and travelers. Based on prior principle, detectors should be deployed on this area firstly.

Similarly, the new road and the congestion road all have priorities.

C. Minimal Edge Control Set Method [5]The first people who use graph theory to study the problem of urban traffic flow detector layout is Weizhen Gu. He modeled a transportation network by a digraph. In his paper, the prob-lem of where to place the traffic detectors becomes finding a minimal edge control set from a graph.

Let G be a digraph. For each vertex v of G, let E1 1v 2 1or E2 1v 2 2 be the set of arcs with tail at v:

E1 1v 2 5 5uv [ E 1G 2 : u [ V 1G 2 6E2 1v 2 5 5uv [ E 1G 2 : u [ V 1G 2 6.

A function f : E 1G 2 S R1h 506 is called a flow of G if

ae[E1 1v2f 1e 2 5 a

e[E2 1v2f 1e 2 for all v [ V 1G 2 .A subset F of E 1G 2 is called an edge control set if, for any

two flows f1 and f2 of G. It is always true that f1 1e 2 5 f2 1e 2 for all e [ F implies that f1 1e 2 5 f2 1e 2 for all e [ E 1G 2 .

If a traffic detector is placed on each edge in an edge control set of the traffic network G, it follows from the defi-nition of an edge control set that the detectors would pro-vide complete traffic information for the network.

All edge control set F is said to be minimal if any proper subset of F is not an edge control set of G. Obviously, it has characteristics stated above. Let G be a digraph such that each arc is on a directed cycle. A minimal edge control set F is to be constructed by the following steps:

Step 1. Let F :5F and H :5G.Step 2. While E 1H 2 2 F, pick any edge e [ E 1H 2 , let

F :5 F h 5e6, H :5H2 CH 1e 2 .Then F is a minimal edge control set of G.There are some problems must to be noticed when to

use minimal edge control set principle. Let 1) G be a digraph and f a flow on G. It is proved that f 1e 2 5 0 if e is not on any directed cycle.


It was assumed in Gu’s paper that the traffic flow within 2) the network contains neither sinks nor sources namely: For all v [ V 1G 2 ,

ae[E1 1v2f 1e 2 5 a

e[E2 1v2f 1e 2 .Minimal edge control sets are not unique. 3) Minimal edge control set principle is more suitable

for expressway network and urban arterial road, and we must make following assumptions when turning a traffic network into a graph:

Using a directed edge 1) e to replace bidirectional link for all road of network and guaranteeing e on a directed cycle.The different weight will be given to links according 2) to service condition (such as average volume) of them. The edge control set with largest sum of weight will be chosen.The method of minimal edge control set studies traffic

flow detector layout based on topology of network. It is con-venient because of less demand of traffic flow data.

D. Comparability Analysis Method [7]City transportation network is an organic whole. There is correlativity among the traffic volume of segments in the road network based on the observations and analysis of traffic parameters. Also the variety of traffic state of most segments has comparability since the residents have simi-lar travel behavior. So we can use comparability analysis to design the traffic detectors space distribution on urban arte-rial roads [2]. Similarity of different roads makes it possible that the traffic flow information of one road can represent the state of other similar roads. So the number of detectors can be cut down based on the conclusion.

Comparability coefficient: Comparability coefficient is 1) used to describe the degree of similarity in traffic vol-ume of two basic road sections

r 1X, Y 2 5 cov 1X, Y 2sXsY

sX5Å 1na

n

i511Xi2 X 2 2

sY5Å 1na

n

i511Yi2 Y 2 2

,

where X is the traffic volume array of link X, Y is the traffic volume array of link Y, r 1X, Y 2 is the comparability coefficient of links X and Y (in the same time interval). If r 1X, Y 2 5 1, links X and Y are comparable, otherwise X and Y are not comparable. The calculation of com parability coefficient of links X and Y should have enough samples (n . 50).

The detector layout model based on 2) comparability analysis

Comparability analysis make it possible to reckon the traffic volume of two or more basic links based on one detector’s data. So how to arrange the layout of traffic detector in urban road system to get the larger possible links traffic flow information using smaller possible detectors? The following model answers this question:

min Z5 an

i51Ci

Xi i5 1, 2, . . . , n

s.t. an

j51aji

Xj $ 1 i, j5 1, 2, . . . , n

Xj5 e 1 has detector in link j0 has no detector in link j

aji5 e 1 when i and j is similar 0 when i and j is similar

where n is the number of basic links; i, j are indexes of links; Xi is a variable representing detector installing state; Ci is deployment coefficient, representing the importance of link i. aji is comparability matrix.

The following application conditions should be noticed when using comparability analysis method:

There are links with comparability in urban traffic network.1) There is at last one detector in similar links so as to supply 2) sample data of traffic flow.

E. Traffic Flow Guidance PrincipleThe Advanced Traveler Information System (ATIS), as a major part of ITS, is to reduce travelers’ travel cost un-certainty with recurrent network congestion through pro-vision of traffic information. The path choice of travelers equipped with ATIS is actually the most important aspect of ATIS. The difference of the information received by travelers will lead to different travel choice behaviors. As the data source of ATIS, the traffic flow detector system should provide rational and accurate real-time traffic flow information for use in guiding travelers. With the information from traffic detectors, travelers should know not only where is the congested area but also the traffic flow state of substitute route.

Based on the principle, the parallel route and supporting road of important urban arterial roads or urban expressways should be given prior consideration so that to supply accurate real-time traffic information to travelers.

The five principles given above can be used to choose area and roads that should install detectors. The following principles will be used to solve problems such as how many detectors should be installed, where to install and which kind of detector should be installed on one road.

F. Crucial Spots Have PriorityResearch has shown that considering the appropriate estimation error for travel time and reasonable investment, the space between detectors should have a suitable range


and the high-density layout of detectors is not always good for efficient traffic information supply [8].

Especially for urban expressways, the traffic flows are stable compare to urban roads because of the close charac-teristics of them. So the distance between detectors should be determined based on the topology and congestion state of expressways. The bottlenecks, inflexions, ramps and frequently congested spots will be taken into consideration firstly. The traffic detectors deployed on inflexion and ramps of urban expressway are shown in Figure 1.

G. Installation Condition Has PriorityDifferent kind of traffic detector desires different installa-tion conditions. For example, travel time detector devices need overpass buildings such as pedestrian bridge, over-head viaduct etc. And for Microwave Traffic Flow Detector (MTFD) which will be installed near by road, the distance from the nearest outer lane to the device is 4 m at least, as shown in Figure 2.

Circumstance is one of the crucial factors must be consid-ered when choosing the position to install detector. Especially for urban arteries and expressways, new building is often not allowed on them because of it’s strongly influence on traffic flow. So, pedestrian bridges, trestles etc. already be there can be used firstly to install travel time detector devices. If there is no such building but enough space nearby the road, the MTFD can be selected.

IV. Application Case: The Detector Layout of Beijing

A. Status Quo AnalysisPrinciples stated above had been used in densifying design of Beijing traffic flow detection system. In this system, the types of fix detectors include MTFD and travel time detector.

The fixed traffic flow detector system has been installed on Beijing transportation network is shown in Figure 3 and Figure 4. The number of each kind of detector in different kind of roads is shown in Table 1.

The main problems of travel time detection system built on Beijing transportation network are:

Some crucial roads in which travel time information 1) need by ATIS have no detectors installed, such as the fourth loop line and the third loop line.The distance between two travel time detectors is too 2) long to supply exact travel time information, such as the fifth loop line, the largest distance between two detectors is 10.1 km (from JinYuanQiao to XiangQuanQiao).The problems stated above lead to the absence of effec-

tive data and the evaluated error of travel time.The MTFDs are installed at intervals of about 700 m

which is a reasonable distance. But in urban arteries, the number of MTFD is inadequate. There are many important roads have no detectors installed.

FIG 3 Travel time detection system installed in Beijing.

*the roads marked by the heavy thread denote that they are installed the travel time detector by the distance of 1–10 km between two detectors.

Partition Ramp

Detectors

Ramp

FIG 1 Traffic detector layout on inflexion and ramps of expressway.

Camera Lens of Video Traffic Flow Detector Cabinet

MTFD

Cabinet

8.584 m

40 m

4.50 m

FIG 2 Setting sketch map of travel time detector and MTFD.


B. A Case of the Travel Time Detector Layout —On the Third Loop Line

Choice reason: Prior principle—The important road has 1) top priority;Distance and position principle: Crucial spots have pri-2) ority and installation condition has priority;Design project3) The number of detectors: Nine (bidirectional);The installation positions of detector: MaDianQiao—

SuZhouQiao—XingXinQiao—LiuLiQiao—YuQuanYingQiao —MuXiYuanQiao—FenZhongSiQiao—GuoMaoQiao— SanYuanQiao

The reason of the design. 4) The loop lines are the important roads of Beijing Trans-

portation network. There are MTFDs with density of two detectors per 1.4 km. They collect traffic flow information such as speed, density, volume etc. Based on this informa-tion, travel time information can be estimated. But consid-ering the evaluated error because of using the point speed provided by MTFDs to calculate average speed between two detectors, travel time detectors are set to gain more accurate

data. MTFDs and travel time detectors cooperate in harmony to offer more accurate traffic flow information. Considering MTFDs installed and configuration of the third loop line, nine travel time detectors are inserted, all on pedestrian bridges or overhead viaducts.

Type of Detector Type of Road

Travel time detectorLoop line Urban road Expressway

16 123 12

MTFDLoop line Urban road Expressway

307 58 96

Table 1. The number of detector installed in Beijing

transportation network.

FIG 6 The traffic scene on MaDianQiao.

The Installation Position of the Detector

FIG 7 Installation position of MaDianQiao.

FIG 5 MaDianQiao panorama (From GoogleEarth).

FIG 4 Microwave traffic flow detection system installed in Beijing.

*the roads marked by the heavy thread denote that they are installed the MTFDs by the distance of 400–1,000 m between two detectors.


MaDianQiao is a typical example shown in Figure 5. It is the crossing of BaDaLing expressway and DeShenMen-Wai avenue and an important bridge. We can see the traffic flow state at this site from the Figure 6. And the installation site of MaDianQiao is shown in Figure 7.

Additional description.5) Based on traffic flow guidance principle and considering

congestion state and installing condition of the third loop line, the travel time detectors are installed not only on main road but also on supporting road so as to get more detail traffic flow information of the loop line including main and supporting links with less cost.

Two other important bridges, SuZhouQiao and LiuLiQ-iao, are used to show how to design the de tectors layout in the third loop line. Figures 8 and 9 are panoramas (from GoogleEarth) of these two places. Installing positions are shown in Figure 10 and Figure 11, two pedestrian bridges over the main road and supporting road.

V. ConclusionsAs an outcome of long-time case studies, a set of principles and methods are proposed in this paper that can be used to

determine easily the density and positions of detectors in prac-tical applications, together with a real example illustrating the whole process of choosing areas, roads, links and positions for detector installation. Of course, these principles should be chosen according to the actual network and road status.

References[1] K. S. Chan and W. H. K. Lam, “Optimal speed detector density for the

network with travel time information,” Transport. Res. A, vol. 36, pp. 203–223, 2002.

[2] G. Jiang and R. Zhang, “Travel time prediction for urban arterial road,” IEEE Trans. Intell. Transport. Syst., pp. 1459–1462, 2003.

[3] L. Cheng, “The using of gray cluster analysis method in the space op-timize of the detectors,” J. Chan’an Univ. (Natural Sci. Ed.), vol. 5, no. 3, pp. 5–6, 2004.

[4] P. T. H. Ya and D. Huang, “Optimization of fixed traffic detector de-ployment based on data mining technology,” Transport. Comput., vol. 23, no. 5, pp. 17–21, 2005.

[5] W. Gu and X. Jia, “On a traffic control problem,” in Proc. IEEE Com-puter Society 8th Int. Symp. Parallel Architectures, Algorithms and Net-works, 2005, pp. 510–515.

[6] J. Zhou, Z. Sheng, and J. He, “Optimal location of traffic counting points for estimating OD trip matrix,” Acta Autom. Sin., vol. 26, no. 3, pp. 303–309, 2000.

[7] J. Wu and F. Wang, “Study on optimal layout of traffic detector for traf-fic data collection system in urban area,” J. Highway Transport. Res. Develop., vol. 21, no. 2, pp. 88–91, 2004.

[8] H. Chu, X. Yang, K. Li, and Z. Wu, “Optimum method for loop detector layout density for expressway based on travel time estimation,” J. High-way Transport. Res. Develop., vol. 23, no. 5, pp. 84–87, 2006.

FIG 10 Installation site of SuZhouQiao. FIG 11 Installation site of LiuLiQiao.

FIG 8 SuZhouQiao panorama (From GoogleEarth). FIG 9 LiuLiQiao panorama (From GoogleEarth).

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On the layout of fixed urban traffic detectors: an application study