ORCID Profile
0000-0002-5845-2929
Current Organisation
Virginia Tech
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Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 06-2019
Publisher: Hindawi Limited
Date: 11-01-2021
DOI: 10.1155/2021/8812740
Abstract: Unpredictable and heterogeneous weather conditions and road incidents are common factors that impact highway traffic speeds. A better understanding of the interplay of different factors that affect roadway traffic speeds is essential for policymakers to mitigate congestion and improve road safety. This study investigates the effect of precipitation and incidents on the speed of traffic in the eastbound direction of I-64 in Virginia. To the best of our knowledge, this is the first study that studies the relationship between precipitation and incidents as factors that would have a combined effect on traffic stream speeds. Furthermore, using a mixture model of two linear regressions, we were able to model the two different regimes that the traffic speed could be classified into, namely, free-flow and congested. Using INRIX traffic data from 2013 through 2016 along a 25.6-mi section of Interstate 64 in Virginia, results show that the reduction of traffic speed only due to incidents ranges from 41% to 75% if the road is already congested. In this case, precipitation was found to be statistically insignificant. However, regardless of the incident impact, the effect of light rain in free-flow conditions ranges from insignificant to a 4% speed reduction while the effect of heavy rain ranges from a 0.6% to a 6.5% speed reduction when the incident severity is low but has a roughly double effect when the incident severity is high.
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2017
Publisher: Elsevier BV
Date: 05-2014
Publisher: Elsevier BV
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: SAGE Publications
Date: 2015
DOI: 10.3141/2489-02
Abstract: Automatic identification of traffic congestion is an important component of any intelligent transportation system. These systems need computer algorithms to identify current congestion and to predict the evolution of future congestion. The output of the congestion identification algorithm enables various users to be better informed and make safer, more coordinated, and smarter use of transportation networks. A new automatic congestion identification algorithm was proposed it assumed that the speed data were drawn from a two-component mixture model. The first component represented the speed distribution in congestion, and the second component was the free-flow speed distribution. The proposed algorithm was first calibrated by using historical speed data in a two-component mixture model. A free-flow speed threshold based on the estimated parameters of the free-flow speed distribution was set. Subsequently, a road segment was identified as having free flow if its speed was greater than the threshold and congested if its speed was less than the threshold. The mixture components considered lognormal and gamma-skewed distributions and normal symmetric distributions. The proposed algorithm was tested by using two real data sets collected from two different roadways and was demonstrated to produce good performance.
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 10-2013
Publisher: Springer International Publishing
Date: 06-07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: ASTES Journal
Date: 06-2017
DOI: 10.25046/AJ020387
Publisher: IEEE
Date: 09-2015
DOI: 10.1109/ITSC.2015.65
Publisher: Informa UK Limited
Date: 08-07-2021
Publisher: Informa UK Limited
Date: 13-05-2019
Publisher: IEEE
Date: 06-2017
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 10-2015
Publisher: Elsevier BV
Date: 2015
Publisher: IEEE
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: MDPI AG
Date: 25-10-2020
DOI: 10.3390/RS12213508
Abstract: As the Autonomous Vehicle (AV) industry is rapidly advancing, the classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes significant training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227 × 227 images to be used for AlexNet and SqueezeNet and constructing 224 × 224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. Moreover, we trained resnet101 and shufflenet for a very short time using one epoch of data and then used them as weak learners, which yielded 98.49% classification accuracy. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.
Publisher: Network Design Lab - Transport Findings
Date: 03-01-2019
DOI: 10.32866/6801
Abstract: Bike sharing systems (BSSs) are being deployed in many cities because of their environmental, social, and health benefits. To maintain low rental costs, rebalancing costs must be kept minimal. In this paper, we use BSS data collected from the San Francisco Bay Area to build a Markov chain model for each bike station. The models are then used to simulate the BSS to determine the optimal station-specific initial number of bikes for a typical day to ensure that the probability of the station becoming empty or full is minimal and hence minimizing the rebalancing cost.
Publisher: IEEE
Date: 10-2019
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 11-2018
Publisher: Springer International Publishing
Date: 2019
Publisher: MDPI AG
Date: 06-07-2021
DOI: 10.3390/S21144636
Abstract: Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.
Publisher: MDPI AG
Date: 04-12-2021
DOI: 10.3390/SU132313433
Abstract: City bikes and bike-sharing systems (BSSs) are one solution to the last mile problem. BSSs guarantee equity by presenting affordable alternative transportation means for low-income households. These systems feature a multitude of bike stations scattered around a city. Numerous stations mean users can borrow a bike from one location and return it there or to a different location. However, this may create an unbalanced system, where some stations have excess bikes and others have limited bikes. In this paper, we propose a solution to balance BSS stations to satisfy the expected demand. Moreover, this paper represents a direct extension of the deferred acceptance algorithm-based heuristic previously proposed by the authors. We develop an algorithm that provides a delivery truck with a near-optimal route (i.e., finding the shortest Hamiltonian cycle) as an NP-hard problem. Results provide good solution quality and computational time performance, making the algorithm a viable candidate for real-time use by BSS operators. Our suggested approach is best suited for low-Q problems. Moreover, the mean running times for the largest instance are 143.6, 130.32, and 51.85 s for Q = 30, 20, and 10, respectively, which makes the proposed algorithm a real-time rebalancing algorithm.
No related grants have been discovered for Hesham Rakha.