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Elhenawy M.,Electrical and Computer Engineering MC 0111 | Rakha H.A.,500 Transportation Research Plaza
21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World

Congestion is a challenge that commuters have to deal with on a daily basis. Consequently, predicting the future status of a roadway is valuable for travelers in making better travel decisions. The deployment of stationary sensors and the proliferation of mobile vehicle probes provide researchers with a wealth of historical and real-time data that can be used for the automatic prediction of congestion along freeway segments. In this paper we introduce anew algorithm for the automatic prediction of congestion using Adaptive Boosting machine learning classifiers. The proposed algorithm creates the learning dataset by identifying congested sections using a skewed distribution mixture model of speed data to create a binary congestion matrix. The elements of this binary matrix are then used as responses in the training of the classifiers. The predictors for the classifier during the training phase are windows (slots) of the historical spatiotemporal speed matrix. In the real-time running phase, the classifiers use the most recent spatiotemporal speed matrix window to predict the short- and medium-term (up to 100 minutes into the future) status of the roadway. Experimental results using archived data from a 22-mile section of the northbound Interstate 5 (I-5) corridor in the Portland, Oregon, metropolitan region demonstrates promising high true positive and low false positive rates. Specifically, using a relatively large number of weak learners (between 20 and 30 learners) the achieved true positive prediction rate is slightly greater than 0.99 and the false positive rate is less than 0.0001. Source

Elhenawy M.,Electrical and Computer Engineering MC 0111 | Chen H.,500 Transportation Research Plaza | Rakha H.A.,500 Transportation Research Plaza
21st World Congress on Intelligent Transport Systems, ITSWC 2014: Reinventing Transportation in Our Connected World

Accurate prediction of dynamic travel times can assist commuters in making better travel decisions. In this paper, a new algorithm is proposed to accurately predict the expected and confidence levels of dynamic travel times. The algorithm pre-processes the available historical data to identify recurring bottlenecks along the road. Subsequently, the algorithm builds a spatiotemporal congestion probability distribution. This distribution provides the probability of a spatiotemporal section being congested. The proposed algorithm integrates congestion probability and spatiotemporal speed measurements to construct feature vectors that are used as the travel time predictors. A random forest is used to model the relationship between the predictors and the travel time. Consequently, the built random forest can be used to predict the travel time by propagating the new features vector through all trees. The experimental results show that the proposed algorithm achieves more than a 38 percent reduction in the prediction error on congested days compared to the state-of-practice instantaneous algorithm and 28 percent reduction when compared to a genetic programming travel time prediction algorithm. Moreover, the predicted travel time bounds encompass all field observations. Source

Ahn K.,Virginia Polytechnic Institute and State University | Rakha H.A.,500 Transportation Research Plaza
Transportation Research Part D: Transport and Environment

This paper quantifies the system-wide impacts of implementing a dynamic eco-routing system, considering various levels of market penetration and levels of congestion in downtown Cleveland and Columbus, Ohio, USA. The study concludes that eco-routing systems can reduce network-wide fuel consumption and emission levels in most cases; the fuel savings over the networks range between 3.3% and 9.3% when compared to typical travel time minimization routing strategies. We demonstrate that the fuel savings achieved through eco-routing systems are sensitive to the network configuration and level of market penetration of the eco-routing system. The results also demonstrate that an eco-routing system typically reduces vehicle travel distance but not necessarily travel time. We also demonstrate that the configuration of the transportation network is a significant factor in defining the benefits of eco-routing systems. Specifically, eco-routing systems appear to produce larger fuel savings on grid networks compared to freeway corridor networks. The study also demonstrates that different vehicle types produce similar trends with regard to eco-routing strategies. Finally, the system-wide benefits of eco-routing generally increase with an increase in the level of the market penetration of the system. © 2013 Elsevier Ltd. Source

Fiori C.,Virginia Polytechnic Institute and State University | Ahn K.,Virginia Polytechnic Institute and State University | Rakha H.A.,500 Transportation Research Plaza
Applied Energy

The limited drive range (The maximum distance that an EV can travel.) of Electric Vehicles (EVs) is one of the major challenges that EV manufacturers are attempting to overcome. To this end, a simple, accurate, and efficient energy consumption model is needed to develop real-time eco-driving and eco-routing systems that can enhance the energy efficiency of EVs and thus extend their travel range. Although numerous publications have focused on the modeling of EV energy consumption levels, these studies are limited to measuring energy consumption of an EV's control algorithm, macro-project evaluations, or simplified well-to-wheels analyses. Consequently, this paper addresses this need by developing a simple EV energy model that computes an EV's instantaneous energy consumption using second-by-second vehicle speed, acceleration and roadway grade data as input variables. In doing so, the model estimates the instantaneous braking energy regeneration. The proposed model can be easily implemented in the following applications: in-vehicle, Smartphone eco-driving, eco-routing and transportation simulation software to quantify the network-wide energy consumption levels for a fleet of EVs. One of the main advantages of EVs is their ability to recover energy while braking using a regenerative braking system. State-of-the-art vehicle energy consumption models consider an average constant regenerative braking energy efficiency or regenerative braking factors that are mainly dependent on the vehicle's average speed. In an attempt to enhance EV energy consumption models, the proposed model computes the regenerative braking efficiency using the instantaneous vehicle operational variables. The proposed model accurately estimates the energy consumption, producing an average error of 5.9% relative to empirical data. The results also demonstrate that EVs can recover a higher amount of energy in an urban driving environment when compared to high speed highway driving using the proposed model. Moreover, the study also compared different electric vehicles and quantified the impact of auxiliary systems, including the air conditioning and heating systems, on vehicle energy consumption levels using the proposed energy model. The study demonstrated that the use of the heating and air conditioning system could significantly reduce the EV efficiency and travel range. © 2016 Elsevier Ltd. Source

Elhenawy M.,500 Transportation Research Plaza | Chen H.,Virginia Polytechnic Institute and State University | Rakha H.A.,Virginia Polytechnic Institute and State University
Transportation Research Part C: Emerging Technologies

The current state-of-practice for predicting travel times assumes that the speeds along the various roadway segments remain constant over the duration of the trip. This approach produces large prediction errors, especially when the segment speeds vary temporally. In this paper, we develop a data clustering and genetic programming approach for modeling and predicting the expected, lower, and upper bounds of dynamic travel times along freeways. The models obtained from the genetic programming approach are algebraic expressions that provide insights into the spatiotemporal interactions. The use of an algebraic equation also means that the approach is computationally efficient and suitable for real-time applications. Our algorithm is tested on a 37-mile freeway section encompassing several bottlenecks. The prediction error is demonstrated to be significantly lower than that produced by the instantaneous algorithm and the historical average averaged over seven weekdays (p-value <0.0001). Specifically, the proposed algorithm achieves more than a 25% and 76% reduction in the prediction error over the instantaneous and historical average, respectively on congested days. When bagging is used in addition to the genetic programming, the results show that the mean width of the travel time interval is less than 5. min for the 60-80. min trip. © 2014 Elsevier Ltd. Source

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