Development of Delay Estimation Methodusing Probe Datafor Adaptive Signal Control Algorithm

Hisatomo Hanabusa1 , Morihisa Iijima1, Ryota Horiguchi1 ,i-Transport Lab. Co., Ltd.,
3F, Jimbocho1-4BLDG, Kanda Jimbocho 1-4, Chiyoda-ku, Tokyo, 101-0051, Japan{hanabusa, iijima, rhoriguchi}@i-transportlab.jp
1

Abstract. This study describes a real time delay estimation method using probe
data with adaptive signal control algorithm, “CARREN” (Control Algorithm
Retuning paRameters with self performance EvaluatioN) [1]. So far, CARREN
supposed the use of AVI (Automatic Vehicle Identifier) data to estimate thetotal delay on approaching each intersection. In this paper, the application ofCARREN using probe data in place of AVI data is explained subsequently.
After the explanation, the real time delay estimation method is validated using
traffic simulation. In the conclusion section, the performance of the proposed
method will be discussed with respect to the penetration rate of probe vehicles,
as well as the issues to be tackled in the future.
Keywords: Adaptive Signal Control, Probe Data, Total Delay Estimation

1Introduction

The purpose of this study is to develop a delay estimation method using probe data fora real time signal control algorithm. The conventional traffic signal control systems
used in Japan design signal parameters based on the degree of saturation. In this case,the optimal signal parameters are selected from pre-determined sets to manipulate thecurrent traffic conditions. However, the systems can only adapt to limited conditions
because of the limited number of pre-determined sets of signal parameters. In addition,a signal control based on the degree of saturation does not guarantee a minimizationof traffic congestion cost (=delay) as an original purpose. Therefore, It is importantthat the delay time is used for the adaptable signal control in real time. For examples,MODERATO (Management by Origin-Destination Related Adaptation for TrafficOptimization) calculates the signal parameters to minimize the estimated delay timeusing the traffic volume from detectors. However, the estimated delay time is notmeasured directory. SCOOT (Split, Cycle and Offset Optimising Technique)estimates the queue length using detector data and evaluates by calculating the degreeof saturation. SCOOT also does not measure the delay time directory. In thesemethods, the evaluation indicators based on the degree of saturation are used todecide the signal parameters. To estimate the indicators, the parameters used in thecalculation of the degree of saturation such as the saturation flow rate and the travel
speed are assumed (not measured in real time).
The delay estimation method in this study is applied to the real time signal
control algorithm CARREN for the validation in the traffic simulator AVENUE (anAdvanced & Visual Evaluator for road Networks in Urban arEas). CARREN uses
AVI systems and detectors to evaluate delay. AVI is the device to observe the plate
number of the passing vehicles. This system can measure the travel time of the section
between AVIs by the plate number matching process. However, the cost of the AVI
system such as installation and maintenance is high. Therefore we discuss thepossibility application of probe data in place of AVI system.
In recent years, various applications using probe data have been discussed suchas the estimation of traffic congestion and vehicle behavior analysis for new
technology and services in the field of road traffic. On the other hand, a variety ofprobe data has been collected and data processing techniques for probe data have beendeveloped. It may be said that probe data is expected to be easily-available by thegrowing of automotive telematics services. For example, if the precision of probe data
is relatively precise, the stop and go status of the vehicle can be discriminated. The
method described by Horiguchi, et al.[2] The probe information we assume here has
the link-wised linear trajectory data identified by the entry/exit times of each link. In
this case, we should consider how to estimate the total delay in real time from theprobe data according to the penetration of the probe vehicle.

2Algorithm of CARREN

CARREN is the real-time signal control algorithm proposed by Koshi, et al. They
described that CARREN is used on the premise of observing travel times of all
passing vehicles and the traffic volume by AVI systems and detectors. The algorithm
decides signal parameters (split, offset and cycle) using the total delay time for eachstream. Fig.1 shows the image of the total delay evaluation of CARREN. AVI(A) andAVI(B) are located at upstream side and downstream side of the target intersection.
The detector is located at the same position of AVI(B). From AVI system, travel
times of the passing vehicle between AVI(A) and AVI(B) is observed. The exit timeof the section can also be observed from AVI system. From the detector, traffic countwith passing times of the vehicle is observed. Using the traffic count data, thecumulative departure curve can be drawn.
Following the observation, the cumulative arrival curve is drawn by the travel
times, the exit times and the cumulative departure curve to calculate total delay time.
First, the position of each vehicle on the cumulative departure curve (=departureposition) is plotted by the exit time. After drawing the cumulative departure curve, thearrival time of each vehicle is calculated by the travel time. Then the cumulative
arrival curve is drawn by the arrival positions excluded the free flow travel time.
Therefore, the area enclosed by the cumulative arrival curves and the cumulative
departure curve in Fig. 1 is the total delay time.

Fig.1 Estimation of total delay by signal
CARREN finds the best combination of split, offset and cycle to minimize thetotal delay time. Fig. 2 shows the evaluation image of the signal parameter change. To
evaluate the parameter change, CARREN estimates the new cumulative curve when
the split and the offset are increased or decreased by increments. After the estimation,
the relationship between the original total delay time and the estimated total delaytime is organized to the matrix of Table 1. f in Table 1 is total delay when the split
and the offset are changed (-Δ or +Δ). For example, if the split is decreased, the arrival
cumulative curve is extended and the cumulative departure curve is translated for Δ
seconds. On the other hand, if the split is increased, the cumulative arrival curve isshorter than the original one and the cumulative departure curve is translated for –Δ
seconds. Therefore, the area of total delay is decreased.

Fig. 2 Evaluation image of signal parameter change using the cumulative curves

Table 1.

Split

Matrix of the total delay time by changing the signal timing

－Δ
0＋Δ－Δ
f-f0f-+

Offset
0
f-0
f00=0
f+0＋Δ
f-+
f0+f++In CARREN, the matrices of the total delay time are calculated for all directionsof the inflow of each intersection. Fig.3 shows the image of the split adjustment.
Basically, signal controller has some signal phases to control the traffic flow from thedifferent directions. In addition, signal controller has to consider the cooperation withthe neighbor intersections. Therefore, the total delay times of each signal in the target
section are compared to each other, and searched the better combination to meet therequirements. The best combination of signal timing parameters can be determinedwhen the all of f are evaluated. The application method of CARREN for practical use
described by Asano, et al. [3] [4]Fig. 3 Split adjustment

3

Delay Estimation using Probe Data

In the first section on this chapter summarizes the issues for the delay estimation
using probe data. The subsequent section explains the assumptions and the procedureof the delay estimation method which we propose.
There would be three issues when we develop the delay estimation method usingprobe data for CARREN. The first issue is how to draw the cumulative departurecurve from the detector data. The cumulative departure curve is very important toidentify the order of the departure of probe data. The second issue is how to draw thecumulative arrival curve with the imperfect link traverse data from the probe vehicles.
The AVI data used in the original CARREN may provide the perfect information ofall vehicles. However, the probe data only provides some samples of link traverses. Itis obvious that we may draw the cumulative arrival curve with fairly reasonableaccuracy when the penetration rate of probe vehicles is high enough. Therefore, themain issue is how to make the drawing method that is feasible when the probe
penetration is low. The third issue is the time lag in the data transmission from probevehicles to the signal control system. In general, the time lag may lose the accuracy ofdelay estimation when the traffic conditions are varying time by time. The time lag indata transmission should be considered for the practical examination.
Fig.4 shows the image of the delay estimation method. To estimate the total
delay time, the cumulative arrival curve and the cumulative departure curve should bedrawn. The cumulative departure curve is drawn by detector data located atdownstream side of the intersection. When we assume that a certain percentage ofpassing vehicles are probe vehicles, the passing time at intersection and the arrival
time estimated by travel time consist of a part of cumulative arrival curve arrival
curve and cumulative departure curve. At this point, cumulative arrival curve is notdrawn.

Fig. 4 System for total delay estimation
If probe vehicles are evaluated, the total delay of all passing vehicle isunderestimated. Therefore, passing time of all vehicle at intersection is observed
using detector (pulse data) to draw departure curve correctly. On the other hand, apart of arrival curve can be drawn by the passing time at intersection and link travel
time. This method consists of probe data fusion process and arrival curve estimation
process. Since the probe data provides link entry time and link travel time, thecumulative arrival curve can be approximated by interpolating the points of the probeentry times on the cumulative flow diagram. Fig. 5 shows the image of the probe data
fusion process and the arrival curve estimation process in total delay estimation
method. The following steps describe the process of the total delay estimation.
1) Plot arrival point of probe and departure point of probe on departure curve(departure curve is drawn by passing time of detector) every cycle. Arrivaltime on arrival curve is excluded free flow travel time.
2) Repeat the step of 1) for specified cycles (parameter of the method) andintegrate past arrival curves and past departure curves over the specified cycles.
(probe data fusion process)3) Approximate arrival curve by two lines. (arrival curve estimation process)4) Estimate total delay by cycle using the approximated arrival curve.
5) Repeat from step 1) to step 4) for every cycle.

Fig. 5 Fusion of cumulative curve
Here, we simplify the arrival curve with two approximated lines. The reason isthat we assume that cumulative arrival curve has two characteristics when traffic is
controlled by systematic signal control. The first one is that the first arrival patternfrom the upstream intersection can be in saturation flow for a few second to several
tens of seconds. The second one is that the second arrival pattern can be in free flow
traffic with demand flow rate after the saturated flow (the first arrival pattern). In thesituation of saturated flow, the angle of the first approximate line should be equal tosaturation flow rate. On the other hand, the angle of the second approximate line
depends on the flow rate of demand and it should be smaller than the angle of the first
approximate line. Adding to this, the total delay consists of one approximate line issimultaneously evaluated. Then we can have a variety of combination of approximatelines and select the best lines. To select the best approximate lines, RMSE (RootMean Square Error) between the approximated lines and the arrival points using
observed link travel times from probe data is calculated for each lines. Finally, thecombination of lines that minimizes the RMSE is selected and is used for the delay
estimation.

4Implementation of the delay estimation method

In this study, we use the traffic simulation model for the validation of the delay
estimation method. Therefore the method should be installed as a part of CARREN.
Fig. 6 shows the basic procedure of CARREN. N is the number of cycle for the datacollection (obtain probe data and detector data). The method is installed between thedata collection process and the signal timing update process. This procedure isexecuted every simulation scan (1 second) for each intersection.

Fig 6. Procedure of CARREN and the estimation method

5

Test Bed for Validation

As a test bed of this study, we have built a simulation dataset for Yasukuni-dori inTokyo. Table 2 shows the overview of the test bed (Yasukuni-dori test bed). We
surveyed traffic volume and signal timing for each intersection to develop Yasukunidori test bed. The travel times of the target section was also observed. This data isused as the input data for the traffic simulator and the validation data for thereproduced traffic situation. The OD traffic volume (Origin-Destination trafficvolume) is estimated by the traffic volume data.
Table 2.

Outline of the Yasukuni-dori test bed.

Network size
Simulation time
Total number of demand
Schedule of OD traffic volume
Type of vehicle

1.8km * 0.5km (61 links, 32 nodes)
From 7:00 to 19:00 (12 hours)
127029 vehicles
Every 10 min.
Small vehicle and large vehicle

The traffic simulator used here is AVENUE. AVENUE employs the ‘Hybrid Block
Density Method (HBDM)’ (Horiguchi et al. 1994). In the HBDM, each link is divided
into several blocks. The in/out–flow and the density of each block are revised at every
scanning interval base on the flow-conservation law and the Q-K (Capacity andDensity) relationships. Fig. 7 shows the display of Yasukuni-dori test bed andAVENUE.

Fig 7. Test bed on AVENUE

6Validation of Delay Estimation

For the validation of the delay estimation method, one link section between theintersection A and the intersection B is chosen (the direction is from A to B). Fig. 8shows the validation section. The link has 3 lanes with right turn lane. We set adetector on the downstream side of intersection B. As the probe data, the link entry
time and the link exit time of probe vehicle are observed.
Fig. 9 shows the comparison result between the total delay estimation method
and the total delay time of all vehicles. We simulated for 12 hours (from 7:00 to19:00) and compared the estimated total delay times from the probe data with themeasured total delay times (the total delay time from all vehicles as the true value) inevery cycle. We examined 12 simulation cases changing the parameter combinations
depending on the probe penetration and the number of cycles for the probe datacollection.
The three figures in Fig. 9 describe that the accuracy of the estimated delay times
tend to be high when the probe penetration or the number of cycles is high. When theprobe penetration was relatively higher as much as 50-70%, the estimated delay times
seem to correlate with the measured delay times. On the other hand, the estimateddelay times were underestimated when probe penetration was relatively lower as
much as 5-10%.

Fig. 8 Section for validation

Fig. 9 Estimation result

The reason of the underestimate includes that the estimation method tends to choose
the approximated arrival curve consisted of one line when the probe penetration islow. If the one-line approximated arrival curve is chosen, the delay time of the firstsome vehicles in the saturated flow are shortened to the point on the arrival curve (thedelay time includes the stop time by signal and the waiting time of the queue).
Therefore, the real delay times in saturated flow might not be considered. In addition,
the validation result was greatly varied in each case. The reason includes that theestimation method uses all probe information that is collected in few cycles for thetotal delay estimation. To solute the problem, we think that the data cleansing toexclude the unsuitable samples for the total delay estimation should be installed.
In this study, we simulate the Yasukuni-dori test bed with CARREN and thedelay estimation method by AVENUE. The calculation time is less than 1 second per
one scan (The scan interval of AVENUE is 1sec). Therefore, we expect the estimation
method is adaptable for the real time signal control.

6Future Works

In this paper, we explained a delay estimation method by using probe, and wedeveloped a test bed of traffic simulation for the validation. Then the validation resultof the method is showed in a part of the simulation network by AVENUE. As for theimmediate future works, we are going to install some data processes, for example,
cleansing process to exclude unsuitableness data. On the other hand, the method will
be improved by considering the Kinematic Wave theory that describes thefundamental characteristics of traffic flows. Using the test bed and CARREN, thecapability of the application of probe information will be validated considering effectof penetration of probe car and difference from signal timing in real.

References
1. R. Horiguchi and K. Wada: Effective Probe Data Transmittal with Detection of Congestion
Pattern, Proceedings of 11th World Congress on Intelligent Transport Systems (2004).
2. M. Koshi, M. Kuwahara, H. Akahane, H. Ozaki, K. Yasui, H. Nakamura, T. Oguchi, T.
Yoshii, R. Horiguchi, H. Oneyama: A Traffic Signal Control Algorithm using ITS Sensing
Technologies: Committee of Infrastructure Plannning and Management, JSCE(2002).
3. M. Asano, A. Nakajima, R. Horiguchi, H. Oneyama and M. Kuwahara: Traffic Signal
Control Algorithm Based On Queuing Model Using ITS Sensing Technologies, Proceedingsof 10th World Congress on Intelligent Transport Systems (2003).
4. M. Asano, A. Nakajima, R. Horiguchi, H. Oneyama, M. Kuwahara, M. Koshi, H. Akanahe:
A Real Time Traffic Signal Control by Self-Evaluating Delay: Japan Society of Traffic
Engineering Vol.20,No.4,pp.879-886 (2003). (In Japanese)
5. Traffic Simulation System AVENUE, http://www.i-transportlab.jp/products/avenue