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.
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.
Congestion on freeway facilities is a growing menace. Interstate 4 (I-4) in the Central Florida region has been experiencing delays during peak hour; this has warranted research on traffic management strategies. The public, through the media, had proposed removing tolls on state toll roads to divert traffic from I-4. A microsimulation model, Paramics, was used to examine the potential impact of this proposal. SR-417 is a relatively uncongested toll road alternative to I-4. SR-528 is the east–west toll road connecting SR-417 and I-4. Commuters on SR-417 have to travel 15 mi longer and pay $5 compared with no monetary cost on I-4 for the same trip. The public and politicians are reluctant to toll I-4 to relieve congestion. The results from the simulation indicated that under recurring congestion conditions on I-4, removing tolls on SR-417 and SR-528 would not divert enough traffic from I-4 because of the 15-mi advantage. Under incident and lane closure scenarios on I-4 with toll reduction on SR-417 and SR-528, the travel time would increase on I-4. This result would prompt some diversion, with volumes and travel times increasing on SR-417. It was concluded that the amount of traffic that would be diverted from I-4 to the toll roads would not significantly relieve congestion on I-4. When specific origin–destination pairs were analyzed, average travel time savings on I-4 were only around 5 min. It was concluded that contrary to the media and public perception, toll reduction would only have a minimum impact on reducing I-4 congestion.
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