**Byron J. Gajewski***

The University of Kansas

**Laurence R. Rilett**

Texas A&M University and Texas Transportation Institute

**Michael P. Dixon**

University of Idaho

**Clifford H. Spiegelman**

Texas A&M University and Texas Transportation Institute

The demand for travel on a network is usually represented by an origin-destination (OD) trip table or matrix. OD trip tables are typically estimated with synthetic techniques that use observed data from the traffic system, such as link volume counts from intelligent transportation systems (ITS), as input. A potential problem with current estimation techniques is that many ITS volume counters have a relatively high error rate. The focus of this paper is on the development of estimators explicitly designed to be robust to outliers typically encountered in ITS. Equally important, standard errors are developed so that the parameter reliability can be quantified.

This paper first presents a constrained robust method for estimating OD split proportions, which are used to identify the trip table, for a network. The proposed approach is based on a recently developed statistical procedure known in the literature as the L_{2} error (L_{2}E). Subsequently, a closed-form solution for calculating the asymptotic variance associated with the multivariate estimator is derived. Because the solution is closed form, the computation time is significantly reduced as compared with computer-intensive standard error calculation methods (e.g., bootstrap methods), and therefore confidence intervals for the estimators in real time can be calculated. As a further extension, the OD estimation model incorporates confirmatory factor analysis for imputing origin volume data when these data are systematically missing for particular ramps. The approach is demonstrated on a corridor in Houston, Texas, that has been instrumented with ITS automatic vehicle identification
readers.

The demand for travel on a network is usually represented by an origin-destination (OD)
trip table or matrix. A trip is a movement from one point to another, and each cell, *OD _{kj}*, in the table represents the number of trips starting at origin

Nihan and Hamed (1992) demonstrated that outlier data can have a significant impact on OD estimate accuracy, but most OD estimation techniques assume that the input data are reliable. Furthermore, it has been shown that unless inductance loops are maintained and calibrated at regular intervals, the data received from them are prone to error rates of up to 41% (Turner et al. 1999). Faulty volume counts can occur because of failures in traffic monitoring equipment, communication failure between the field and traffic management, and failure in the traffic management archiving system. The first two failure types may be difficult to detect because they occur in isolated detectors. Other detector problems include stuck sensors, chattering, pulse breakup, hanging, and intermittent malfunctioning. Consequently, there is a need to account for the input error in the estimation process. Historically, detector malfunctions have been addressed by "cleaning" the datasets prior to the estimation step. This is problematic for ITS applications, because the data manipulation takes time and the use of these techniques is limited in a real-time environment. Even for off-line applications, cleaning the data is problematic, because it is not always clear which data need to be cleaned and how this can best be accomplished. This paper examines a robust approach whereby the data error associated with the faulty input data is accounted for explicitly within the estimation of the OD trip table.

The development of OD estimators that are robust to detector malfunction has been largely ignored in OD estimation research, with the exception of an approach proposed using a least absolute norm (LAN) estimator (Sherali et al. 1997). LAN is intuitively more robust to outliers than LS, because the errors are not squared as they are in LS (Cascetta 1984; Maher 1983; Robillard 1975), nor are the data treated as constraints as they are in many maximum entropy and information minimization approaches (Cascetta and Nguyen 1988; Van Zuylen 1980). This paper follows on their work by using estimators explicitly designed to be robust to outliers typically encountered in an ITS environment. Equally important, standard errors will be developed so that parameter reliability can be quantified.

This paper first presents a constrained robust method for estimating OD split proportions, which are used to identify the trip table, for a network. The traffic volumes are assumed to come from an Advanced Traffic Management System (ATMS), and the traditional assumption that the data are "reliable" is relaxed to allow the possibility of missing or faulty detector data. The proposed approach is based on a recently developed parametric statistical procedure known in the literature as the L_{2}
error (L_{2}E).^{1} Subsequently, a closed-form solution for calculating the asymptotic variance associated with the multivariate estimator is derived. Because the solution has a closed form, the computation time is significantly reduced as compared with the computer-intensive calculation of the standard errors (e.g., bootstrap methods and off-line methods based on the LS (Ashok 1996)), and therefore confidence intervals for the estimators in real time can be calculated. As a further
extension, the OD estimation model incorporates confirmatory factor analysis (Park et al. 2002) for imputing volume data when this information is systematically missing for particular ramps.

While the techniques demonstrated here are motivated using inductance loop data, they are easily generalized to other detectors, models, and data. The approach is demonstrated on a corridor in Houston, Texas, that has been instrumented with automatic vehicle identification (AVI) readers. The AVI volumes are used to estimate an AVI OD matrix. The benefit to this test bed is that the actual AVI OD table can be identified and, therefore, provides a unique opportunity for directly measuring the accuracy of the different techniques. The OD matrix can be obtained using a linear combination of the split proportion matrix and the origin (entrance) volume vector.

It should be noted that because the input volumes are dynamic, the estimated OD matrix is also dynamic. However, because the split proportion is assumed constant, the OD matrices by time slice are linear functions of each other.

Also note that while the method is acceptable for freeway networks with relatively short trips between interchanges, such as the example in this paper, an application to a larger network would raise the question as to when the trips began or ended.Caution should be exercised when applying this method to a large proportion of trips that begin and end during different time periods.

The objective of this paper is to estimate the OD split proportion matrix and its associated distribution properties. The split proportion *P _{kj}*

A desirable feature of an OD estimator is the ability to calculate a closed-form limiting distribution. This is particularly important for ITS applications, so that confidence intervals about the estimate can be calculated in real time. The estimator will be defined and distributional properties of the estimator will be derived following the steps below.

- Define the multivariate constrained regression model and objective functions.
- Obtain distributional properties.
- Stack the variables to obtain a univariate constrained regression model.
- Use the equality constraints to obtain a univariate
*un*constrained regression model. - Apply asymptotic theory to derive the distributions of the estimated split proportions for the LS and the L
_{2}E estimation techniques. - Transform the model back to its original form.

**Step 1: Define the Model and
Objective Functions**

A traffic network can be represented by a directed graph consisting of arcs (or links)
representing roadways and vertices (or nodes) representing intersections. There are *q*
origin (entrance) ramps and *p* destination (exit) ramps, and the analysis period is broken
down into *T* time periods of equal
length *t*.
Figure 1 shows an example of a traffic system in
Houston, Texas, along the inbound (eastbound) Interstate 10 (Katy Freeway) corridor and a schematic diagram of the corridor.

The underlying assumption of the OD model developed here is that mass conservation holds. In essence, it is assumed that the traffic entering the system also exits the system within a specified time period. This assumption is appropriate as long as the time intervals are long and/or the traffic is in a steady state. For time period *t, t* = 1, 2,,*T, D _{tj}* denotes the destination volume at destination ramp

The first assumption is that the split proportion *P _{kj}* is constant over the entire analysis period. The second assumption is that the number of vehicles entering the system equals the number of vehicles exiting the system so that the split proportions at each origin during the period sum
to 1 . The expectation of the destination volumes during each time
period

The term is the error.

Intuitively, some of the split proportions are not feasible (*P _{kj}* = 0) because of the structure of the traffic network, and this needs to be incorporated in the estimation process. Note that in some cases the nonfeasible parameters may simply represent unlikely cases. The zeros are exact in the applications presented later.

It is assumed in this paper that the errors in each column are independently and identically distributed with mean zero and constant variance. This means that the error associated with the volume measurement at destination *j* is uncorrelated with the volume measurement at destination *j*. Note that all assumptions will be checked in the applications section of the paper.

Because the OD problem structure is implicit rather than explicit, and because it is also overspecified, the synthetic approaches attempt to estimate the split proportion matrix based on the minimization of an objective function (Dixon 2000). The objective function in equation 2 is based on an LS approach, which is a common way to estimate the split proportions (Robillard 1975).

Note that if is distributed normally then the LS is equivalent to the maximum likelihood estimator.

The central hypothesis of this paper is that the LS method is inappropriate for ITS applications because of the high error rate of the measured volumes. In contrast, the L_{2}E objective function is defined as the integrated squared difference between the true probability density function and the estimated density function. Therefore, it is theoretically more robust, relative to the LS, when the data have outliers such as those caused by detector malfunctions. A more specific discussion of this follows.

As an example, consider a sample of size *T*. If the goal is to estimate the location
parameter, or the central tendency, *O _{t}P*

The normal distribution, , replaces resulting in the objective function displayed as equation 4,

The L_{2}E minimizes the sum of probability density functions (pdfs) while the MLE (or LS) minimizes the negative product of pdfs. In the presence of an outlier, the MLE objective function will multiply a zero and the L_{2}E objective function would add a zero. The relative effect of the outlier on the objective function is much more severe in the case of the MLE. This is because of the multiplicative effect of the zeros on the MLE objective function. Because ITS data often contain outliers, it is hypothesized that the L_{2}E objective function will provide better estimates. For a more detailed discussion of the L_{2}E properties, see Basu et al. (1998) or Gajewski (2000).

When calculating the L_{2}E, an estimate of the variance is required. In this paper, the
estimate of the variance is identified using a two-step process.
First
is calculated using the median of the squared residuals so that an initial estimate of the standard
deviation, ,
may be obtained by calculating the median absolute deviation (MAD) from the set of estimated
residuals, where the *t*^{th} estimated residual
is .
The median of the squared residuals is used because it has been shown to have a high breakdown point (Hampel et al. 1986). In general, the breakdown point is the percentage of outliers in the data at which the estimator is no longer robust (Huber 1981). For example, if the breakdown point is 50% then the estimator is robust to datasets that contain less than 50% outliers.

**Steps 2a-b: Stack and Define
the Unconstrained Univariate Regression Model**

Through reparameterization, the split proportion model shown in equation 1 can be translated into an unconstrained univariate regression model. The first step is to stack the split proportion matrix *P*, where

*P ^{v} = vec*(

For example,

The equality constraints are subsequently defined relative to *P ^{v}* using G

and

The matrix *M* (*r* by *qp*) maps the nonfeasible values of *P ^{v}* to zero. The value

As an example, define

where *P*_{11} = 0 and *P*_{21 }= 0.

Therefore, the product of the *M* and *P ^{v}* matrix maps the nonfeasible elements to zero

where

The first *q* rows of each *G* and *g *are used to normalize the split proportions as shown in equation 5.

Given the constraints defined above and the stacked model, the reduced model may be obtained by solving equation 5. Because *G _{1}* is trivially full rank (

Equation 1 is placed in univariate form by defining *Y* to be the stacked columns of *D* and at the same time
letting
When equation 6 is substituted into the stacked model the unconstrained model is obtained. The substitution is done with the portion of the stacked model that corresponds
to the
regression parameters. If

then

and the new regression model, which describes in univariate form the multivariate regression problem, is defined in equation 7.

where

**Step 2c-d: Variance Estimates for
the Reparameterized Model and Distributions**

These next steps produce distributions for the estimators under the reparameterized model, in particular that both L_{2}E and LS produce estimators that consistently estimate the
true *P ^{v}*
(or ),
and that
is asymptotically normally distributed, under certain assumptions. The asymptotic normality directly produces variance estimates
of .

The asymptotic properties follow directly from the reparameterized model discussed in Steps 2a-b. Then the asymptotic theorems from Huber (1981) from the objective functions (or derivatives of them called *M*-estimators) are applied, written in an unconstrained form.

The only additional assumptions for these asymptotic results are that the errors in equation 1 have a mean of zero and a variance of and are iid. This assumption encompasses many distributions that include heavier tails then the normal distribution. The specific calculations for the asymptotic variances are and,

where
and *U* are defined in the appendix.

One result of this is when the errors are normally distributed the ratio of the variances of the L_{2}E estimator and the LS estimator of any element
of

which says that the variance under the L_{2}E estimator is 1.54 times as high as the LS estimator. Therefore, the L_{2}E estimator is actually less efficient than its LS estimator counterpart. However, by applying a heavy-tailed distribution, the result reverses and the L_{2}E estimator is better than the LS estimator. In the subsequent sections, a simulation demonstrates this result.

The derivation of the asymptotic distributions has shown that explicit closed-form variance estimates exist and that associated standard errors of the split proportions can be calculated in real time. Notice that these theorems can be extended to other types of *M*-estimators, in addition to the L_{2}E, as long as the assumptions are met.

Note that if the assumptions to derive the closed-form standard errors are not appropriate, then bootstrap methods will be required. The bootstrap is a computer-intensive procedure that uses resamples of the original data to obtain properties such as standard errors. One can use various algorithms to do this, such as case resampling or model-based resampling (Davison and Hinkley 1997).

The test bed is an eastbound section of Interstate 10 (I-10) located in Houston, Texas, as shown in figure 1. This section of I-10 is monitored as a part of the Houston Transtar Transportation Management Center (TMC) (operated by the Texas Department of Transportation), the Metropolitan Transit Authority of Harris County (METRO), the City of Houston, and Harris County. There are six automatic vehicle identification readers located in the test bed. As instrumented vehicles pass under an AVI reader, a unique vehicle identification number is recorded and sent to a central computer over phone lines. From this information, the average link travel time is calculated and presented to drivers through various traveler information systems. It may be seen in figure 1 that the I-10 test corridor is made up of five AVI links consisting of six "origins" and six "destinations." Note that each destination may consist of several destination ramps and each origin may consist of several origin ramps, because the AVI links are defined by the location of the AVI readers and not the physical geometry of the corridor.

The OD methodology developed in this paper was motivated by the assumption that the TMC has access to volume information obtained from point detectors (i.e., inductance loops). However, the models are tested using data obtained from an AVI system because both AVI volumes and split proportions can be identified. This provides a unique opportunity for comparing the accuracy of the OD estimates that are based on volumes obtained at point sources with observed OD split proportions. In this situation, the split proportions are estimated using only the OD volumes from the AVI data.

Based on a preliminary analysis, 18 days in 1996 were used for the analysis
(October 1, 2, 4, 5, 15-19, 22-26, 30, and November 1, 6, and
8).^{2}
The AVI vehicles detected at the origin and destination ramps during the AM peak period (7 am to 9 am) were aggregated into 4 volume counts of 30-minute duration. Therefore, the number of time periods (*T*) is equal to 72, the number of destinations (*p*) is 6, and the number of origins (*q*) is 6. The
AVI volumes were used as input to the LS and the L_{2}E estimators and the resulting
estimates are presented in table 1.

The assumption that the split proportion matrix was constant, which was used in the derivation of the LS and the L_{2}E estimators, was verified by examining the observed AVI split proportion differences across all days and all time periods of the study. Because these observed split proportion differences were within 0.1 for 95% of the estimates, it was judged that the assumption was reasonable for this test case. The observed mean AVI split proportions for the AM peak are shown in table 1.

The correlation coefficients between the observed mean AVI split proportions and the estimated split proportions by the LS and the L_{2}E estimators are 0.961 and 0.960, respectively. These results indicate that both estimators are successful at estimating the OD split proportions. However, the mean absolute percent error (MAPE) between the mean observed split proportion and the estimated split proportions using the LS and the L_{2}E estimators were 44.5% and 51.3%, respectively. These results indicate each method has an approximate error of 50% with respect to individual OD split proportion estimates.

A further analysis of the absolute percent error (APE) at the individual OD level showed that the APE decreases as the size of the OD split proportion increases. For example, the MAPE for OD pairs that have observed split proportions in the range of 0-0.10, 0.10-0.30, and greater than 0.48 are 78.1%, 31.2% and 11.9%, respectively, for the LS estimator. The L_{2}E analysis had similar results in that the percentage error for cells that have split proportions in the range of 0-0.10, 0.10-0.30, and greater than 0.48 were 91.5%, 38.8%, and 6.3%, respectively. As would be expected from their objective functions, the estimators tend to have more accurate results for the more "important" OD pairs as measured by split proportion or relative volume. In general, these results are encouraging because the estimates for the OD pairs that have higher split proportions, which will in all likelihood be the more important ones for ITS applications, will tend to be more accurate.

The asymptotic variance calculated using the closed-form solutions developed in this paper and
variance estimators calculated using a bootstrap technique are shown in
table 2.
The bootstrap estimate of the standard error was calculated using 999 estimates of the split proportion. These estimates were calculated from realizations produced from resampling the rows of *D* and *O* (Davison and Hinkley 1997). The APE between the asymptotic standard deviation and the bootstrap standard deviation ranged quite a bit. However, the APE tended to be lower for the OD pairs with higher observed OD split proportions, which was similar to that of the previous analysis.

For the L_{2}E analysis, the APE between the asymptotic standard deviation and the bootstrap standard deviation also ranged quite a bit. Similar to the LS analysis, the standard deviation of the APE tended to decrease as the size of the observed split proportion increased. In general the L_{2}E asymptotic standard deviation gave better results in 13 of the 21 parameters, as compared with the bootstrap standard deviation the LS produced. The asymptotic standard deviation for both the LS and the L_{2}E estimators were, on average, higher than the bootstrap method and can therefore be used as a conservative estimate of the standard deviation in real-time applications.

Based on the estimated split proportions and the variance shown in table 2, it can be shown that there are no statistically significant differences among the observed split proportion values and the estimated values using either the L_{2}E or the LS methods when tested at the 95% level of confidence. This result indicates that either method would be appropriate for this dataset. It is important to note, however, that the AVI volume data are of very high quality with a low error rate, which is not the case for inductance loop data.

The standard deviations of the residuals for columns 1 through 6 are 10.70, 11.92, 7.34, 10.43, 9.13, and 11.26, respectively, which are relatively close. The residuals viewed using a normal probability plot indicate that the hypothesis that the errors are normal can be accepted. Lastly, the off-diagonals of the sample correlation matrix indicated a small correlation between the columns of the residuals. The average absolute value of the correlations is 0.33. Based on these observations, the assumption that the errors are independent and have the same normal distribution (i.e., ) appears valid for this example.

Checking the assumptions allowed us to apply the input-output model presented in this paper to the Houston AVI data. While the AVI example helped validate the methods developed in this paper, it does not illustrate the robustness of the L_{2}E relative to the LS estimator because of the generally high quality of the AVI data. Therefore, robustness is illustrated using a Monte Carlo simulation study motivated from the AVI test bed example. A binomial distribution, with probability parameter *H*, which represents the proportion of contamination, was used to sample the destination volume matrix across the 72 time periods and 6 destinations. Two separate scenarios were examined. In the first scenario, the chosen cells are set to zero, which mimics a detector failure. In the second scenario, the chosen cells are inflated to mimic chatter in the data. The inflated values were set to 3,240 vehicles per half-hour, which is higher than capacity and consequently the values are not feasible. A
Monte Carlo experiment was performed 100 times for each scenario for levels of *H* ranging from 0 to 0.80 in increments of 0.1.

The mean squared error (MSE) as a function of the probability of destination detector failure for
both scenarios is shown in
figure 2.
It can be seen that as proportion of contamination (*H*) increases, the MSE for both estimators also increases under each scenario. Note that for both scenarios the rate of increase in MSE for the L_{2}E estimator is much lower than that of the LS estimator. For example, when the percentage contamination is 20% the MSE for the L_{2}E estimator is approximately 0.1, which is only 10% and 7% of the MSE for the LS estimator under scenarios 1 and 2, respectively. It can also be seen that while the L_{2}E estimator is more robust than the LS estimator for lower values of *H*, after the probability of contamination reaches a certain point both estimators are comparably poor (or approximately flat due to Monte Carlo error).

The sum of the MSE, which may be considered a surrogate for the variance, was subsequently calculated using the simulated data and the asymptotic equations using the uncontaminated data (i.e., *H* = 0). The sums of the MSE for the LS estimator were 0.053 and 0.059 for the simulation and asymptotic analyses, respectively. The sums of the MSE for the L_{2}E analyses were 0.088 and 0.090 for the simulation and asymptotic analyses, respectively. Thus, the values derived using the asymptotic theory approximately equal the values derived from the simulation results and demonstrate the theory derived earlier in this paper.

To study the effects of errors that are not as severe as those in scenarios 1 and 2, and
consequently harder to detect, a second sensitivity analysis was performed. In this case the
percentage error was varied between -75% and 75% and, as before, a binomial distribution was used to
perturb the destination matrix volume measurements.
A sensitivity analysis was performed on the percentage contamination, which ranged from 0.1 to 0.6
in increments of 0.1. As in the earlier experiments, the Monte Carlo simulation was
carried out 100 times for each simulation. The results are presented in
figure 3,
which shows the ratio of the L_{2}E estimator MSE divided by the LS estimator MSE as a function of the percentage of detectors experiencing errors. For example, it can be seen that when the detector error rate is 25% and the probability of a given detector experiencing difficulty is 50% (*H* = 0.5), the relative efficiency is 0.5. That is, the L_{2}E estimator MSE is approximately half of the LS estimator MSE, indicating that the L_{2}E estimator is more robust to detect error for
this scenario.

The second point to note about figure 3 is that when there is no data contamination the MSE of the L_{2}E estimator is approximately 60% larger than the MSE of the LS estimator. It can be seen that as the detector percentage error grows, the efficiency of the L_{2}E estimator relative to LS estimator rises at an increasing rate before it plateaus at approximately an absolute value of 50%. In addition, the choice of when to use the L_{2}E estimator is a function of the expected contamination rate and the expected percentage error in detector accuracy. As shown in figure 3, whenever the relative efficiency dips below 1.0 the L_{2}E estimator would be preferred. For this example, when the percentage error of detector accuracy is less than approximately 15% the LS would be chosen. However, when the detector percentage error is greater than 15%, it can be seen that the L_{2}E estimator would be preferred for all values of *H* less than 0.5.

At this point, it should be emphasized that the L_{2}E approach is a statistical technique that is not limited to an input-output model but can be generalized to any number of OD problem formulations. In the case of this particular problem, other detectors (main lanes with volume recorders that provide additional volume information) could be accounted for by substituting the input-output model with a general link volume model where the output is modified to include mainline volumes. In this way, the L_{2}E estimator would be robust to malfunctions in any of the detectors.

In addition, confirmatory factor analysis can be used when other detectors in the system fail by imputing the missing data (Park et al. 2002). The unknown volumes at each origin are mathematically equivalent to the unknown scores in confirmatory factor analysis. Therefore, when a loop failure occurs, some of the origin volumes are observed and some are not. Therefore, confirmatory factor analysis and the models developed in this paper can be combined to form a mixed model. The observed destination volumes are contained in matrix *D*, the observed origin volumes are in matrix *O*_{2}, the unobserved origin volumes are in matrix *O*_{1}, and the split proportions are placed in matrix *P*.
The mixed model is thus ,
where the split proportions are

partitioned .

For instance, consider the AVI example previously discussed. Suppose the volume counts at origin one or origin six cannot be observed because of detector failure. Therefore, origins two through five correspond to *O*_{2} and origins one and six correspond to *O*_{1}.

Under the notation described above, the least squares objective function becomes

Next we solve for *O*_{1} and minimize the objective function with respect to *P*. The unknown portion of *O* is *O*_{1},
thus .
Therefore the objective function is

Estimates from this objective function produce a sequence that converges to the fixed but unknown true parameter (Gajewski 2000).

*Remark:* Confirmatory factor analysis occurs when all of the regressors are unknown resulting in *P*_{2} being empty. The objective function, similar to confirmatory factor analysis, is

,

where

.

Ordinary least squares regression occurs when *P*_{1} is empty and

In most cases, model identifiability of the parameters holds because of the number of nonfeasible OD pairs. The biggest challenge is meeting the assumption
where is full rank,
assuming is the matrix composed of the columns containing the assigned zeros in the *k*th row with those assigned zeros deleted. The nonfeasible OD pairs arise from the fact that a vehicle cannot have certain origin or destination combinations. Details of model identifiability and rank are found in Park et al. (2002).

Note that the LS method is used to demonstrate the approach, because the AVI data were found to
be well behaved. It should be noted that under the conditions presented in the theory section of
this paper .
That is, as *T* goes to infinity the estimator under the mix model using least squares converges to the true split proportions (Gajewski 2000).

A test of the imputation technique on data from the test bed was subsequently performed. It was assumed that the volume from the first origin and the last origin are missing and the techniques from confirmatory factor analysis were used to impute the missing origin volumes. Table 3 shows the results, and it can be seen that the average percentage error for origins one and six are -30% and 3%, respectively. It can also be seen that the APE was lowest for the origin ramps that had the highest volume.

The important feature of the above technique is that it provides a consistent estimator for the split proportions despite missing a portion of the input information. While there are other imputation techniques (Chin et al. 1999; Gold et al. 2001), it is not clear if these techniques provide consistent estimators. Therefore, the above algorithm can be used when input information is missing or in situations where the input information is known a priori to be faulty. In this latter case, the faulty input volume would be removed prior to the estimation of the split proportions.

*Remark:* The formulation derived in this paper was presented in OD format. Alternatively, in the data imputation format, the origins, *O*, can be treated as output parameters and the model
formulated as
This is a DO formulation where the split proportion *P _{kj}* represents the proportion of vehicles exiting at

The OD matrix was calculated for the full AVI data under both formulations. The OD matrix, under the DO formulation, was calculated using the LS estimator. The correlation between the OD matrix calculated from the DO model and the OD matrix calculated from the OD model is 0.9018; the correlation between the OD matrix calculated from the DO model and the true OD matrix is 0.9514; and the correlation between the OD matrix from the OD model and the true OD matrix is 0.9293. Therefore, the OD matrices from both formulations were approximately equal for this test example.

Origin-destination matrices are an integral component of off-line traffic models and real-time advanced traffic management centers. The recent deployment of ITS technology has resulted in the availability of a large amount of data that can be used to develop OD estimates. However, these data are subject to inaccuracies from a variety of sources. In this situation, the data may be "cleaned" and then used with existing OD estimators. Alternatively, OD estimators may be developed that are robust to the outliers characteristic of ITS data. This latter approach was the focus of this paper.

A robust estimator based on the L_{2}E theory was first derived and compared with a traditional least squares estimator. In addition, closed-form asymptotic distributions for the variance were derived for both estimators. This is an important contribution because it allows variances, and therefore standard errors, to be calculated for the estimates. It was shown that while the L_{2}E estimator was less efficient than the LS estimator, it was more statistically robust to bad data.

The models were applied to a corridor on Interstate 10 in Houston, Texas. The test bed was instrumented with AVI technology and therefore both AVI volumes and split proportions are available. This test bed provides a unique opportunity for comparing the estimators because an observed OD matrix is available, which is extremely rare for these types of studies. The asymptotic variance estimates were found to be slightly conservative as compared with the estimates derived using a bootstrap method. It was shown that while the LS and the L_{2}E estimators had average mean absolute error ratios of approximately 50%, both replicated the observed OD at the 95% level of confidence. In addition, the accuracy of the estimates was highest for the OD pairs with higher relative volumes.

A Monte Carlo simulation was carried out to test the robustness of the techniques under different error types and error rates. It was found that the L_{2}E estimator was much more robust than the LS estimator to outliers. However, after a certain threshold, which was 50% on the I-10 test network, the models were found to perform equally poorly. Lastly, a method for imputing missing origin data was developed and illustrated using the LS estimator.

It should be noted that the L_{2}E approach is not limited to the input-output model used in this paper, but rather can be generalized to any number of OD problem formulations. For example, information from other detectors, such as those commonly found on main lanes, could also be added as a dependent variable and a consistent OD estimator could be formulated. In this way, the L_{2}E estimator would be robust to malfunctions in any of the detectors. In addition, it was assumed in the model derivation that there was no prior information regarding the split proportion matrix. The model can also be generalized to incorporate prior information, which would help add "structure" to the over-parameterized model and could potentially reduce the percentage error of the estimates.

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**Notation**

The following notation serves as a reference guide throughout this paper:

*T* Number of time periods. Each time period is of
duration *t*.

*p* Number of destinations in the system.

*q* Number of origins in the system.

*r* Number of nonfeasible elements of *P*.

*O* Matrix of observed volumes from *q* origins over *T* time periods (*T* by *q*).

*D* Matrix of observed volumes from *p* destinations over *T* time periods (*T* by *p*).

*O _{t}* Row vector of observed volumes from

= 1,2,3,,

*D _{t}* Row vector of observed volumes from

*O _{tk}* Element (

*P* Split proportion matrix between *q* origins and *p* destinations (*q* by *p*).

*P _{kj}* Proportion of vehicles that enter at origin

*P _{j}* Column vector of

Matrix of random errors with *n* rows and *p* columns.

*A ^{v}* Vectorization of matrix

Stacked version of .

Row vector of
*p* elements from the *t*^{th}
row of , *t* = 1,2,3,,*T*.

*P ^{v}* Stacked version of

*M* Matrix that maps the elements of *P ^{v}* that are nonfeasible to zero.

*O ^{v}* Stacked version of the matrix

Kronecker product .

*X*^{*} The regressors used in the stacked
model, , *X*^{*} is *Tp* X *qp*.

*G* Equality constraint matrix.

G = [G_{1}G_{2}] Partition of *G* into matrices *G*_{1} and *G*_{2}, where *rank*(*G*) = *rank*(*G*_{1})

= *q* + *r*, and *G*_{1} is (*q* + *r*) X (*q* +* r*). *G*_{2} is (*q *+ *r*) X (*pq-q-r*)

Note that *G* is permuted until *G*_{1} is full rank.

*G*_{1} The nonsingular portion of the constraint matrix *G*.

*G*_{2} The "left over" portion of the constraint matrix *G*.

Partition
of *P ^{v}* such that the elements correspond to [

The portion of *P ^{v}* that corresponds to

The portion of *P ^{v}* that corresponds to

Partition
of *X*^{*} such that the elements correspond to [*G*_{1}*G*_{2}].

1* _{q}* Column vector of

0* _{r}* Column vector of

g *GP ^{v}* =

Variance for each row of .

*Y*_{2} (*Tp* X 1).

*Y*_{2t} The *t*^{th} element of *Y*_{2}.

*W*_{2}

(*Tp* X (*pq-q-r*)).

*W*_{2t} The *t*^{th} row vector of *W*_{2}.

*Z* *Z = Y _{r} / *. (

*Z _{t}* The

*U* (T* _{p}* X (

*U _{t}* The

*W*_{2tk} The (*t,k*) element of *W*_{2}.

(*Tp* 3 1).

Estimated vector of using least squares.

Estimated
vector of using L_{2}E.

Influence function (see Hampel et al. 1986).

Normal distribution with mean and variance .

**Detail Theory**

**Step 1**

*Condition 1.* The split proportions (*P*) are positive for all feasible origin and destination combinations and equal to zero for all nonfeasible combinations.

Therefore the general model is written algebraically as follows:

and

The detailed derivation of the L_{2}E objective function is

Because the third component in the second line of equation A1 does not have the optimization parameter *P* (but only the true parameter *P*_{0}), it can be taken as a constant and ignored during optimization. The empirical density function is used to estimate the expectation in the second line of equation A1 (Scott 1999).

Details of the following results appear in Gajewski (2000). The error structure for the reduced problem is shown in equation A2.

Therefore, equation A3 provides the estimate of the reduced regression parameters under LS, and it can be seen that it is unbiased.

The variance of this estimate is shown in equation A4.

Note that equations A3 and A4 are based on the assumption that *W*_{2} is full rank. Lemma 1 relates the rank of the origins, *O*, to that of the rank of *W*_{2}.

*Lemma 1:* If *O* is full rank, then *W*_{2} is full rank. (See Gajewski (2000) for proof.)

Note that it is assumed that there will be multiple days worth of data and multiple time periods in each day. Therefore, the variation of volume between days and within days causes *O* to be full rank and therefore *W*_{2} is full rank.

Similar to the variance of the estimates in equation A4, the variance estimates for are presented in equations A5 and A6.

The covariance between the estimates and are shown in equation A6.

Notice that the reparameterization causes the problem to be unconstrained except for the non-negativity constraints. When deriving the asymptotic distributions of the estimators , it is assumed to be in the interior of the parameter space. From a physical viewpoint, this means that the true split proportion over the long term would never lie on the boundary (i.e., be equal to zero) for a feasible OD pair. It is assumed that this assumption is valid on the highway networks where most ITS traffic monitoring devices are located. In addition, if this assumption is violated, the origins and destinations can be combined to alleviate the problem. Regardless, this assumption will need to be checked when applying the model.

The asymptotic distributions for the two estimation methods are derived using properties of
*M*-estimators (Huber 1981). Equation A7 shows the L_{2}E model. As
discussed, is
estimated prior to using equation A7. Let *N*(*A,B*^{2}) be the normal
probability density function with a mean of *A* and variance *B*^{2}, then

with

A reparameterized model may be obtained by using the variance to standardize the model. Let and . Therefore, the reparameterized reduced model will be where and the objective function, is shown below:

Because the L_{2}E is an *M*-estimator, the distribution properties are straightforward to derive and will be useful in the derivations of the distribution.

*Definition 1:* (Huber 1981): For a
sample *x*_{1},*x*_{2},...,*x _{T}* from the sample
space

The L_{2}E estimator is an *M*-estimator because of a calculation of the derivative of the objective function.

*Property 1:* is an *M-*estimator (see Gajewski (2000) for proof).

Huber (1981, p. 165) proves the asymptotic properties for the reduced model of the form . Note that this proof assumes and the three regularity conditions, referred to as RC1 to RC3, are met.

*Property 2:* Regularity conditions RC1 to RC3 hold for the OD problem (see Gajewski (2000) for proof).

Theorem 1 shows the distribution of the LS estimator in a linear model setting is asymptotic normal.

*Theorem 1:* (Huber 1981, p. 159):

If , then all the LS estimates are asymptotically normal.

Theorem 1 states that asymptotic normality holds for both objective functions in this paper. Therefore, the identification of the mean and variance define the asymptotic distribution.

Using Theorem 1, the LS variance is calculated as shown in equation A10.

Similarly, the variance for the L_{2}E is shown in equation A11.

Note that, in order to use equation A11, the errors need to be identified. For example, if it is assumed that the error distribution is normal, Property 3 shows the exact value of the ratio .

*Property 3:*
Suppose , then

and therefore

(See Gajewski (2000) for proof.)

If the error distribution is non-normal then an approximation is used, as shown in the Remark.

*Remark:* The nonparametric estimation of the above equation is:

(See Gajewski (2000) for proof.)

The asymptotic distributions are stated in Corollary 1 for completeness.

*Corollary 1:* Let *a* be a fixed vector. Under the conditions of the above models and the regularity conditions RC1-RC3, equations A12 and A13 are the asymptotic distributions for the LS and L_{2}E objective functions,
respectively:

(See Gajewski (2000) for proof.)

Based on the distributional properties shown in Corollary 1, the L_{2}E method is less efficient than the LS method as shown in Corollary 2.

*Corollary 2:*
When , then for every *b* (fixed vector),

(See Gajewski (2000) for proof.)

Note that the nonparametric formula shown in the Remark is preferred when the parametric distribution of the error is not known.

Byron Gajewski, Schools of Allied Health and Nursing, 3024 SON, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160. Email: Bgajewski@kumc.edu.

^{1} Basu et al. (1998) and Scott (1999) contributed the idea to the parametric literature. L_{2} refers to the squared distance between two points. The L_{2}E was originally used in kernel density estimation (see, e.g., Terrell 1990).

^{2} See Dixon (2000) for specific data extraction techniques.