2001 IEEE Intelligent Transportation Systems Conference Proceedings - Oakland (CA), USA - August 25-29, 2001
An Arterial Speed Estimation Model Fusing Data from Stationary and Mobile Sensors Ruey Long Cheu', Der-Homg Lee, and Chi Xie
Absrruct-This paper presents an arterial speed estimation model using data from two distinct sources: mobile probe vehicles and inductive loop detectors. The model consists of three modules: (1) probe vehicle module which measure arterial speed using vehicles equipped with differential global positioning system receivers; (2) loop detector modules which estimate link speed using loop detector data, incorporating traffic signal timing parameters; and (3) data fusion module, which uses a neural network to combine outputs from the above two modules to improve the speed estimation accuracy. The computational procedures of the three modules are presented. This paper presents a validation test of the model using a set of data generated from a calibrated simulation model. Our test results show that, the probe vehicle and loop detector modules are capable of making speed estimation with 2-RMSE of less than 3.20 km/h. Using a neural network to fuse the estimates from the two sources reduces the 2RMSE to less than 1.32 kmlh.
positioning systems (DGPS) receivers, over the nation's The road network of approximately 3,000 km. measured average speeds at different links, translated into color codes, are disseminated to the public at a web site called Trafficscan (httu://www.trafficscan.Ita.gov.sg). Recently, the LTA has also awarded a contract to install JunctionEyes video traffic monitoring system for 100 intersections. In JunctionEye, each intersection approach is equipped with a video camera to collect traffic data for surveillance purpose and to input into the traffic signal control system. With the above systems in place or progressively operational, it is increasing important to fuse all the traffic speed and travel time information together. It is with this background that the research described in this paper is carried out.
Index Terms-arterial speed, loop detectors, probe vehicles, Global Positioning Systems, data fusion, neural networks
Several researchers have conducted experiments on the use of GPS in measuring travel time [I]-[3]. Little research has been conducted on data fusion for speed estimation. Here, speed is chosen over travel time because the former is independent on the link length, and users have a better perception on speed then travel time. The purpose of our arterial speed estimation model is to make use of speed data obtained from two types of sensors: mobile probes (i.e., vehicles equipped with DGPS receivers) and stationary sensors (i.e., loop detectors), to produce a more accurate estimation than using a single type of sensor alone.
I. INTRODUCTION With the progressive implementation of ITS user services, it is increasingly important that accurate estimation of link speed and travel time be provided to travelers and traffic managers. The accuracy of travel time estimate has a positive influence on the effectiveness of ITS control strategies, and user services.
The entire arterial speed estimation model consists of three modules: probe vehicle (PV), loop detector (LD) and data fusion (DF) modules. The first two modules each uses its own data source to make estimation of arterial speed. The probe vehicles, of limited number, measure arterial speeds (termed space-mean speed) over the entire link length. Loop detectors measure the speeds of all vehicles at a fixed spot on a link (termed time-mean speed or spot-speed). The DF module combines the speed estimation outputs from PV and LD modules to make a more accurate estimation. The methodologies and accuracy of using probe vehicles and loop detectors, respectively, to estimate arterial speed have been investigated in separate papers [4], [ 5 ] . The focus of this paper is to present the overall data fusion architecture, and results of a validation test using simulated data.
In Singapore, the Land Transport Authority (LTA) provides travel time information from major expressway entrances to major interchanges via variable message signs (VMS) in its Expressway Monitoring and Advisory and Systems (EMAS). The travel times are calculated from individual linkkegment speeds measured by loop detectors. In addition, the LTA is acquiring real-time positioning and speed information from 10,000 taxis equipped with differential global
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The authors are with Department of Civil Engineering, National University of Singapore, Singapore 1 17576, Singapore (e-mail:
[email protected]).
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arterials, L I ranges from 141 m to 410 m, while L2 range:; from 40 m to 60 m. Loop detectors are coded in every lane, from 90 m to 150 m upstream of the stop lines (denoted by b in Fig. 1). The loop detectors measure average cruisl: speed and volume count. It is assumed that loop detectors are within the field of view of the JunctionEye cameras;. Some traffic control systems (e.g., SCOOT [SI) locate their loop detectors in the mid-section or upstream end of a link, which permit this type measurement.
Before we discuss the neural network data fusion technique, it is necessary to present (I) the data used in our experiments; (2) the PV module; and (3) the LD module.
SIMULATION AND DATAPREPARATION 11. TRAFFIC In our model development and testing, the performance criterion is the root-mean-square error (RMSE) in estimated speed. This error is the difference between the average speed of all vehicles that have traversed a link (hereafter referred to as all-vehicle speed,) and the output of either the PV, LD, or DF modules. In field test, all-vehicle speed can only be approximated from a large number of observations. In view of the varied traffic environment we want to test the model on (i.e., different link length, PV sample size, traffic volume, and etc), it is decided to base our model development and testing on simulated data. The Version 2.0 of INTEGRATION, a microscopic traffic simulation model capable of simulating probe vehicles and loop detectors, was employed for this purpose [6].
It should be noted that, occasionally, vehicles may start to decelerate in the upstream segment in response to th.e signal downstream, or, the queue may spill back onto the upstream segment. In this case, the probe vehicle data will capture part of the signal delay. In LD module, an equation has been developed to factor this delay into the travel time within L I .
The road network of the Clementi area, a sub-urban residential cum commercial district in Singapore, has been modeled. The link speeds along two major arterials, namely Commonwealth Ave West and Clementi Road are of interest. These major arterials have three through lanes in each direction. The baseline traffic volume was the moming peak period from 8:OO a.m. to 9:OO a.m. in a typical weekday. The signal plans and intersection tuming volume were obtained from LTA.
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Fig. 1. Segmentation of link and detector placement. The rNTEGRATION model was calibrated with field travel time data along Commonwealth Ave West and Clementi Road, where arterial speeds are of interest. The calibrated model is able to simulated probe vehicle travel times, with mean and standard deviation values not significantly different from field data obtained through license plate surveys. Details of the calibration are documented in [7].
The PV link speed is computed from location differencing approach (i.e., dividing the distance between two points by travel time). It was assumed that each active DGPS receiver continuously surveys its position, say at one or two-second intervals, and compared this with an on-board digital roadmap database. When a vehicle is located in a desirable position, the position and time stamp are recorded. Link speeds can then be calculated and stored in the on-board memory, and later transmitted to the traffic management center at the end of a sampling period. The DGPS approach may be supplemented or replaced by roadside positioning devices, such as beacons.
In INTEGRATION modeling, the number of PVs for a particular origin-destination (0-D) pair in the network was specified as a percentage of the total 0 - D volume. Once a PV is generated, the INTEGRATION program traces and records its movement from the origin to destination. Individual PV’s statistics, such as the travel time and average speed at each link was directed to an output file.
A total of 216 simulation runs were conducted in INTEGRATION under a variety of traffic settings. These varied conditions included (a) six different 0 - D volumes, i.e., at the based 0 - D in the moming peak period, and 60%, 70%, 80%, 90%, ;and 110% of the based 0-D; (b) six different percentages of PVs in the total 0-D volume, i.e., PV%=3%, 6%, 9%, 12%, 15% and 18%; and
Refer to Fig. 1, each link in the major arterial is divided into upstream and downstream segments, with lengths LI and L2 respectively. The downstream segment contains vehicle queue during the red signal phase. Since the vehicle queue disrupts travel time and speed measurement, it was decided not to include the downstream segment (Lz) in our speed measurement. The probe vehicles were modeled to measure travel time only over L I . In our
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(c) six different degree of randomness in vehicle headway generation, to reduce the bias of using the default random number seed.
the standard deviation of sampled probe speeds. To be consistent with Step I , the confidence level I-a is set to 95% and the absolute error limitation E, is set as 5 km/h. If the above equation gives a sample size less than or equal to the probe vehicles available, the speed estimate is still accepted as the probe vehicle module’s output (although the number of used probe vehicles is less than 10). Otherwise the average speed is deemed inaccurate and rejected.
In each simulation, a warm-up period of 500 seconds was first carried out, followed by a data collection interval of 700 seconds. The 700-second interval was selected because (i) it is within the practical range of pooling frequency for communication between the vehicles and the management center, and (ii) it is a multiple of signal cycle time of 140 seconds. For the eight links of interest, the individual travel time of probe vehicles, traffic volume and average speed from loop detectors, and all-vehicle speed were extracted for analysis. The extracted data consists of 1,728 observations, was used for the development of testing of PV, LD and DF modules.
The accepted speed estimate from probe vehicle with sufficient sample size, together with the sample size, are used as inputs into the DF module.
MODULE IV. LOOPDETECTOR
A different validation data set was assembled to test the entire speed estimation model. This validation data set is extracted from similar simulation runs, except that ( I ) the data was from five other links along the same comdors; and (2) the network consisted of only 3% and 6% probe vehicle. Such proportions comply with the current probe vehicle market penetration in Singapore.
The arterial travel time (over segment length L I ) is divided into two components: travel time = cruise time -+ signal delay
(2)
where cruise time is derived by dividing the segment length ( L I )by speed (U,obtained at the detector station) The signal delay is calculated by the simplified Webster formula [ 1 I]
111. PROBEVEHICLE MODULE A. The simulated probe vehicle data has been analyzed in detail in [4]. The data showed that, the accuracy of average probe vehicle speed increases with the sample size. It was concluded that, if the absolute error in the estimated average link speed is to be less than 5.0 k d h at least 95% of the time, there should be at least I O prove vehicles within a sampling period. This finding is consistent with a separate study on a freeway in New York/New Jersey with travel time data collected using toll tags [9]. From the above results, and from the comparison with the Standard Deviation Formulation [IO], a two-step data screening procedure has been proposed for the PV module: Step I : for a particular link, if the number of probe vehicle is at least IO, the average speed computed from these probe vehicles is regarded as accurate enough and is used as output of the probe vehicle module; otherwise, proceed to Step 2; Step 2: apply the Standard Deviation Formulation to check if the probe vehicle sample size is equal to or more than the required number determined by
signal delay = 0.94
[%$+&I
(3)
Here, C is the cycle time in seconds, g is the effective green in seconds, q is the traffic volume in veh/hr/lane, A represents the effective green proportion (g/C), and x represents degree of saturation ((qC)l(sg)), with s being the saturation flow rate in veh/hr/lane. The factor q5 is to portion the signal delay into the link segment L,.
(4)
Detailed derivation of the above equation, which is based on deterministic queuing theory, can be found in [12]. As soon as the average travel time has been estimated by this model, the average link speed can then be calculated. The proposed model, named Singapore model, relies on loop detector data (U and q), link geometry (L1, L2.and b) and signal timing parameters (C and g ) as input. The saturation flow rate (s) is a preset constant. Therefore, there is no link specific parameter that needs to be calibrated. All the inputs are available in the traffic signal
where ta,2,n-l is the t-distribution statistic for I-a confidence interval with degree of freedom n-I, E, is an allowable absolute error in speed estimates and s is
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control center. This presents an obvious advantage over the British model [13], Iowa model [14], Illinois model [ 151, that need calibrations for link specific parameters. Note that, the Singapore model is also applicable to the case when the downstream end of a link is a stop line (i.e., Lz=O, or el)
700 sec, over 5 different links, with overall PV proportions at 3% and 6% at the network level.
During model development, the Singapore model gave estimation errors in comparable magnitude against the British, Iowa and Illinois models. The Rh4SE error in speed estimation is less than 2.5 k d h . Assume that the error follows a normal distribution with mean 0, this may be interpreted as, at 95% of the time the speed error is less than 5.0 km/h.
When the 360 data points were presented to the two-step heuristic data screening process, 21 3 data points (59%) passed the first step with probe vehicle sample size of at least 10. Another 109 (30%) were deemed to have sufficient sample size, when compared to the criteria given by the Standard Deviation Formulation in Step 2. A total of 38 data points (11%) was rejected by the PV module. For these 38 data points, the average link speed could only be estimated by the loop detector module alone.
A . Validation of Probe Vehicle Module
V. DATAFUSIONMODULE
For the 322 data points that have passed the screening test, a comparison was made between the speed output of the PV module and all-vehicle speed in Fig. 2. The RMSE is 1.37 km/h, and the R2 (coefficient of determination) is 0.8074. This is an improvement from RMSE of 1.48 k d h and RZvalue of 0.7786, for the same data set but without data screening.
The neural network paradigm used for data fusion in this research is the commonly used multi-layer feed-forward neural network with backpropagation training [ 161. The neural network consists of three layers. The input layer has three neurons receiving (1) average speed estimated by the LD modules; (2) average speed estimated by the PV module; and ( 3 ) probe vehicle sample size. The latter gives an indication on the accuracy of the average speed calculated from probe vehicle data. The only output neuron produces a continuous value of hsed link estimate. The number of hidden neuron was set to 5 , following the popular 2n-1 rule, where n is the number of input neuron. Each neuron in the hidden and output layer carries a sigmoid transfer function.
y = 10022.x
R’ =
0.8074
RMSE = 1 3 7 k m f i
The neural network was trained with 1032 data points (after data screening, to remove data with insufficient probe vehicle sample size). An adaptive learning rate was used, with 80% of the training data used for weight adjustment and 20% reserved for test against overtraining. Weights were updated at end of each cycle, for 1000 cycles.
( D a n Screening
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To evaluate the performance of the MLF data fusion model, the error between the fused speed estimates and allvehicle link speeds is computed from a subset of data reserved for this purpose. The Rh4SE of speed estimates is reduced to 1 .OS km/h. This is an improvement from using either probe vehicle estimates or detector estimates alone.
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Fig. 2. Comparison between output of PV module and allvehicle speed.
B. Validation of Loop Detector Module VI. MODELTESTING As mentioned earlier, there is no data screening for the loop detector module. Comparison of link speed was made between the output of the LD module, and the all-vehicle speed. From Fig. 3, the LD module also gives a good estimation performance, based on the small RMSE value of 1.60 km/h and a high R2 value of 0.9048. The
Testing of the entire speed estimation model was carried out with the validation data set. This data set consists of 360 data points collected over a same sampling period of
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comparison of RMSEs is given in Table 1 . The error magnitude of link speed estimated by the LD module is relatively slightly higher than that of the PV module, even without data screening.
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TABLE 1 RMSEs of different modules computed from validation data set. PV module LD module DF module RMSE ( W h ) 1.48’ 1.37# 1.60 0.66 * before data screening # after data screening
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Fig. 4. Comparison between output of DF module with all-vehicle speed.
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In this validation test, although the outputs of PV and LD modules are rather accurate, the DF module is still able to further improve the estimation accuracy. From the comparison of RMSEs in Table 1 , it is found that the MLF data fusion algorithm reduces more than 50% of speed estimation errors from single source-based models.
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AND PRESENT WORKS VII. SUMMARY
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This paper has presented a model designed to provide arterial link speed estimation through fusing data from probe vehicles and loop detectors. A neural network model is used to fuse speed data collected from two distinct sources, to make a more accurate estimation of the arterial speed. The model has been developed, tested and validated with simulated data.
Fig. 3. Comparison between output of LD module with all-vehicle speed.
C. Validation of Data Fusion Module
The focus of our current work is to conduct field test to further validate the three modules, and the entire model. In addition, different methods of estimating speed within the PV and LD modules are being investigated. The existing PV module computes link speed based on the location differencing approach. We are investigating the methods of screening and combining instantaneous speed data obtained by probe vehicles directly from DGPS receivers, for accurate link speed measurement. A limitation of the current version of the LD module is the placement of loop detectors, which must be beyond the reach of vehicle queue at intersection most of the time. The use of stop-line detectors, combine with signal timing
Data fusion using the trained neural network model was performed for the 322 data points that have outputs from the PV module. The fused estimates using the MLF approach is plotted in Fig. 4, against all-vehicle speed. The distribution of data points in this figure shows that the accuracy of fused estimate is higher than using PV or LD modules operating alone. The RMSE value has been reduced to 0.66 km/h and R2 value increased to 0.9577.
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W. R. McShane and R. P. Roess, Traffic Engineering, Prentice Hall, Inc., Englewood Cliffs, NJ, 1990. X. Chi, An Arterial Link Speed Estimation Model Using Data from Stationary and Mobile Sensors, M.Eng. Thesis, Dept. of Civil Engineering, National University of Singapore, Singapore, 2000. H. E. Gault and I. G. Taylor, The Use ofOutputfroin Vehicle Detectors to Access Delay in ComputerControlled Area Traffic Control Systems, Research Report 37, Transport Operation Research Grouip, University of Newcastle upon Tyne, U.K. H. M. Zhang, “Link-journey-speed model for arterial traffic.” Transportation Research Record 1676, pp. 109-115, 1999. [I51 V. P. Sisiopiku and N. M. Rouphail, Travel Time Estimation from Loop Detector Data for Advanced Traveler Information Systems Applications, Technical Report of the ADVANCE Project, Illinois University Transportation Research Consortium, Chicago, IL, 1994. [I61 J. M. Zurada, Introduction to Artificial Neural Systems, PWS Pub. Co., St. Paul, MN, 1992.
parameters, to make link speed measurement is also being research into. This will permit the application of the LD model to arterials not covered by the JunctionEye system.
VIII. ACKNOWLEDGEMENT The authors would like to thank the Land Transport Authority of Singapore for providing the relevant traffic signal timing and intersection volume count data for use in this research.
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