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Applications of Deep Learning Models for Traffic Prediction Problems
Title
Applications of Deep Learning Models for Traffic Prediction Problems.
Creator
Rahman, Rezaur, Hasan, Samiul, Abdel-Aty, Mohamed, Zaki Hussein, Mohamed, University of Central Florida
Abstract / Description
Deep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first...
Show moreDeep learning coupled with existing sensors based multiresolution traffic data and future connected technologies has immense potential to improve traffic operation and management. But to deal with complex transportation problems, we need efficient modeling frameworks for deep learning models. In this study, we propose two different modeling frameworks using Deep Long Short-Term Memory Neural Network (LSTM NN) model to predict future traffic state (speed and signal queue length). In our first problem, we present a modeling framework using deep LSTM NN model to predict traffic speeds in freeways during regular traffic condition as well as under extreme traffic demand, such as a hurricane evacuation. The approach is tested using real-world traffic data collected during hurricane Irma's evacuation for the interstate 75 (I-75), a major evacuation route in Florida. We perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as K-Nearest Neighbor, Analytic Neural Network (ANN), Auto-Regressive Integrated Moving Average (ARIMA). We find that LSTM-NN performs better than these parametric and non-parametric models. Apart from the improvement in traffic operation, the proposed method can be integrated with evacuation traffic management systems for a better evacuation operation. In our second problem, we develop a data-driven real-time queue length prediction technique using deep LSTM NN model. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync Adaptive Traffic Control System (ATCS) data to train a Long Short-Term Memory Neural Network model capturing time-dependent patterns of a queue of a signal. To select the best combination of hyperparameters, we use sequential model-based optimization (SMBO) technique. Our experiment results show that the proposed modeling framework performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well. Queue length prediction is a crucial part of an ATCS to optimize control parameters and this method can improve the existing signal optimization technique for ATCS.
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Date Issued
2019
Identifier
CFE0007516, ucf:52654
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007516
Improving Traffic Safety at School Zones by Engineering and Operational Countermeasures
Title
Improving Traffic Safety at School Zones by Engineering and Operational Countermeasures.
Creator
Rahman, Md Hasibur, Abdel-Aty, Mohamed, Lee, JaeYoung, Zaki Hussein, Mohamed, University of Central Florida
Abstract / Description
Safety issues at school zone areas have been one of the most important topics in the traffic safety field. Although many studies have evaluated the effectiveness of various traffic control devices (e.g., sign, flashing beacon, speed monitoring display), there is a lack of studies exploring different roadway countermeasures and the relationship between school-related factors and crashes. In this study, the most crash-prone school zone was identified in Orange and Seminole Counties, Florida,...
Show moreSafety issues at school zone areas have been one of the most important topics in the traffic safety field. Although many studies have evaluated the effectiveness of various traffic control devices (e.g., sign, flashing beacon, speed monitoring display), there is a lack of studies exploring different roadway countermeasures and the relationship between school-related factors and crashes. In this study, the most crash-prone school zone was identified in Orange and Seminole Counties, Florida, based on crash rate. Afterward, a microsimulation network was built in VISSIM environment to test different roadway countermeasures in the school zones. Three different countermeasures: two-step speed reduction (TSR), decreasing the number of driveways (DD), and replacing the two-way left-turn lane (TWLTL) to the raised median (RM) were implemented in the microsimulation. Three surrogate safety measures-: (1) time exposed time to collision (TET), (2) time integrated time to collision (TIT) and (3) time exposed rear-end crash risk index (TERCRI) were utilized in this study as indicators for safety evaluation. The higher value of surrogate safety measures indicates higher crash risk. The results showed that both TSR and DD reduced TET, TIT and TERCRI values significantly compare to the base condition. Moreover, the combination of TSR and DD countermeasures outperformed their individual effectiveness. The One-way ANOVA analysis showed that all the sub-scenarios were significantly different from each other. Sensitivity analysis result has proved that all the sub-scenarios in TSR and DD reduced TET, TIT and TERCRI values significantly for different value of TTC threshold. On the other hand, for converting the TWLTL to RM, the crash risk was higher than the base condition because of the turning movements of vehicle. The results of this study could help transportation planners and decision makers to understand the effect of these countermeasures to improve safety at school zones.
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Date Issued
2019
Identifier
CFE0007708, ucf:52409
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007708