Haitham Al-Deek

Travel-Time Prediction for Freeway Corridors

The application of a nonlinear time series model to the prediction of traffic parameters on a freeway network is investigated. The nonlinear time series approach is a statistical technique that has strong potential for on-line implementation. A new approach for predicting corridor travel times is developed and tested with travel-time data. The travel-time data are derived from observed speed data, which are collected from an 18-km (11.2-mi) freeway section in Orlando, Florida. The westbound Interstate-4 morning peak period (6:00 to 10:00 a.m.) for 20 incident-free days is tested with the goal of predicting recurrent congestion. The problem is addressed from the perspectives of single-variable and multiple-variable prediction of corridor travel times. In single-variable prediction, speed time-series data are used to forecast travel times along the freeway corridor. A calibrated single-variable prediction model is developed through the application of decay factors to smooth out the input data and the establishment of a threshold on the minimum speed prediction permitted. Multivariable prediction schemes are developed using speed, occupancy, and volume data provided by inductive loop detectors on the study section. The prediction performance of the calibrated single-variable model is shown to be superior to the multivariable prediction schemes. This new approach produces reasonable errors for short-term (5-min) travel-time predictions. The developed model can be implemented on-line with minimal effort.

Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models

Short-term traffic prediction on freeways is one of the critical components of advanced traveler information systems. The traditional methods of prediction have used univariate Auto-Regressive Integrated Moving Average (ARIMA) time series models, based on the autocorrelation function of the time series of traffic variable at a location; however, the effect of upstream and downstream location information has been largely neglected or underutilized in the case of freeway traffic prediction. It is the purpose of this article to demonstrate the effect of upstream as well as downstream locations on the traffic at a specific location. To achieve this, a section of five stations extending over 2.5 miles on I-4 in the downtown region of Orlando, Florida is selected. The speeds from a station at the center of this location are then checked for crosscorrelations with stations upstream and downstream. Cross correlation function is analogous to autocorrelation function extended to two variables. It indicates whether the past values of an input series influence the future values of a response series. It was found that the past values of upstream as well as downstream stations influence the future values at a station and, therefore, can be used for prediction. A vector auto regressive model was found appropriate and better than the traditional ARIMA model for prediction at these stations.

Modeling the benefits of advanced traveler information systems in corridors with incidents

Abstract This paper concerns the benefits from Advanced Traveler Information Systems (ATIS) in corridors under incident conditions. A deterministic queueing model is developed and applied to an idealized corridor composed of two routes. A user optimal strategy is implemented to disseminate real-time traffic information to vehicles equipped with ATIS as they approach the incident bottleneck. The sensitivity of route guidance benefits to relevant parameters such as the fraction of vehicles guided with ATIS is analyzed. The findings show that a few cases of queue evolution result when ATIS is used under incident conditions. Both the proportion of guided traffic and the incident duration play an important role in determining which case results. When an incident occurs, ATIS will divert all equipped vehicles to the alternate route until equilibrium is achieved. Equilibrium is achieved only if a sufficient number of guided vehicles comply with diversion instructions. Equilibrium is maintained by reducing the rate of diversion from one route to the other. The implication is that during equilibrium some guided travelers will be diverted to the alternate route, while others will remain on the route where the incident has occurred. It is found that the benefits to guided traffic decrease when the proportion of guided traffic exceeds a critical value that causes a queue on the alternate route. The system benefits also level off once the critical value is exceeded. Therefore, if the system management has the choice, there is no need to equip more than the critical fraction of vehicles with ATIS. There is a need to develop a methodology that can find practical estimates of the critical fraction for use in large-scale simulations of real-life networks.

Using Real-Life Dual-Loop Detector Data to Develop New Methodology for Estimating Freeway Travel Time Reliability

Travel time reliability is an important system performance measure for freeway traffic operations. It captures the variability experienced by individual travelers, and it is an indicator of the operational consistency of a facility over an extended period. Real-life transportation data were used to develop a new methodology for estimating travel time reliability of the 1-4 corridor in Orlando, Florida. Four different travel time distributions were tested: Weibull, exponential, lognormal, and normal. The developed best-fit statistical distribution (lognormal) can be used to compute and predict travel time reliability of freeway corridors and report this information in real time to the public through traffic management centers. When compared with existing Florida and buffer time methods, the new reliability method showed higher sensitivity to geographical locations, which reflects the level of congestion and bottlenecks. Another advantage of the new method is its ability to estimate the travel time reliabil...

NEW ALGORITHMS FOR FILTERING AND IMPUTATION OF REAL-TIME AND ARCHIVED DUAL-LOOP DETECTOR DATA IN I-4 DATA WAREHOUSE

Loop detectors have been used to gather traffic data for over four decades. Loop data diagnostics have been extensively researched for single loops. Loop data diagnostics for the dual loops laid along 63 km (39 mi) of I-4 in Orlando, Florida, are specifically addressed here. In the I-4 data warehouse, dual-loop detectors provide flow, speed, and occupancy every 30 s. The mathematical relationships among flow, speed, occupancy, and average length of vehicles were used to flag bad data samples provided by a loop detector. A value called the entropy statistic is defined and used to determine the detectors that are stuck. Regression techniques were applied to fill the holes formed by the bad or missing samples. Various pairwise regression models were developed and described, and their performance on the loop data from January and February 2003 was analyzed. The best model was identified as the pairwise quadratic regression model with selective median, which is currently being used to impute missing data in real time. Results are presented of the application of these algorithms to archived loop detector data in the I-4 data warehouse.

A SIMPLE MODEL FOR ROUTE GUIDANCE BENEFITS

This paper concerns the benefits from vehicle route guidance in urban networks. We suppose that routes can be altered in such a way as to achieve system optimal assignment. Benefits are measured by the savings in total travel time when comparing this assignment with the user equilibrium, which is assumed to occur in the absence of route guidance. A continuum approach is used to analyze an idealized corridor in which a freeway is superimposed over a dense grid of surface streets. The main role of route guidance is to divert traffic from the freeway whenever its marginal cost exceeds that of the street system. It is found that saving in total system travel time of the order of 3-4% can be achieved from route guidance. Benefits are quite sensitive to city street speed. At low speed more users would choose the freeway resulting in congestion, and the potential benefits of route guidance are relatively high. However, as street speed increases and approaches that of the freeway, route guidance would be of less value as more of the motorists would be choosing the city street on their own. Benefits can be enhanced if information is customized to motorists on the basis of their origins and destinations. Finally, it is shown that benefits are reduced when the freeway network is dense. This paper does not consider important aspects of the evaluation of route guidance, such as the equity issue stemming from increasing some trip times in order to achieve system optimum, or the local impact of diverted traffic.

Preliminary Validation of Driving Simulator Based on Forward Speed

This research was a preliminary step in the process of determining whether the driving simulator at the University of Central Florida (UCF) provides a realistic driving experience. Thirty volunteers from the driving population were asked to drive an instrumented car along a section of road on the UCF campus. A distance measurement instrument provided a log of instantaneous speed, cumulative distance, and elapsed time at designated points along the route for subsequent analysis. The second phase of the research entailed driving in the UCF driving simulator, which consists of a complete vehicle cab with a wraparound screen for displaying computer-generated images of a synthetic road and surroundings. Computer-generated imagery of the identical campus road and environment was viewable to the subjects in the simulator. Drivers were asked to perform the same task in the simulator as they did in the real driving environment. Identical information was acquired during field testing and the simulation runs. Speed data from the field and simulator were analyzed using conventional statistical tests to determine whether drivers responded differently in the simulator compared with their response during the real driving experience. Preliminary results of the statistical analysis indicated that the drivers behaved similarly at 10 of 16 designated locations along the road. Confidence intervals for the difference between the simulator and the field mean speeds indicated a tendency of drivers to travel at slower speeds in the simulator. These results, along with qualitative feedback from the subjects concerning the handling characteristics of the simulator, are being studied to determine the necessary simulator refinements and upgrades required before additional validation testing.

Modeling traffic operations at electronic toll collection and traffic management systems

Abstract Automatic Vehicle Identification (AVI) technology is one possible solution to traffic congestion at existing transportation facilities. This paper presents a mathematical model of traffic conditions for toll plaza facilities that includes AVI toll collection services among other conventional services. Three types of services are available: manual toll service, in which transactions are handled by a toll collector, automatic toll service, in which coin machines are utilized, and AVI toll collection service. In addition, mixed lanes, which provide more than one of the above services, are considered by the model. For a given rush hour, queue lengths and delays can be calculated for different toll plaza configurations. Comparison of their performance may aid operators in the management of the lane configurations until all users of the facility become AVI patrons.

FUZZY ART NEURAL NETWORK MODEL FOR AUTOMATED DETECTION OF FREEWAY INCIDENTS

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

Cross-Correlation Analysis and Multivariate Prediction of Spatial Time Series of Freeway Traffic Speeds

Short-term traffic prediction on freeways is one of the critical components of the advanced traveler information system (ATIS). The traditional methods of prediction have used univariate ARIMA time-series models based on the autocorrelation function of the time series of traffic variables at a location. However, the effect of upstream and downstream location information has been largely neglected or underused in the case of freeway traffic prediction. The purpose of this study is to demonstrate the effect of upstream as well as downstream locations on the traffic at a specific location. To achieve this goal, a section of five stations extending over 2.5 mi on I-4 in the downtown region of Orlando, Florida, was selected. The speeds from a station at the center of this location were then checked for cross-correlations with stations upstream and downstream. The cross-correlation function is analogous to the autocorrelation function extended to two variables. It indicates whether the past values of an input series influence the future values of a response series. It was found in this study that the past values of upstream as well as downstream stations influence the future values at a station and therefore can be used for prediction. A vector autoregressive model was found appropriate and better than the traditional ARIMA model for prediction at these stations.