Browsing by Author "Ashqar, Huthaifa I."

Now showing 1 - 6 of 6
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    Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data
    Elhenawy, Mohammed; Ashqar, Huthaifa I.; Masoud, Mahmoud; Almannaa, Mohammed H.; Rakotonirainy, Andry; Rakha, Hesham A. (MDPI, 2020-10-25)
    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.
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    Evaluation of the Use of a Road Diet Design: An Urban Corridor Case Study in Washington, DC
    Aljamal, Mohammad A.; Voight, Derek; Green, Jacob; Wang, Jianwei; Ashqar, Huthaifa I. (MDPI, 2021-08-11)
    A traditional road diet design converts a four-lane two-way road to a three-lane road consisting of two through lanes and a center two-way left turn lane. This paper introduces a new application of the road diet design in an urban corridor. Specifically, the new application converts a four-lane two-way road into a two-lane two-way road with full-time parking lanes in both directions. The paper analyzed the traffic impacts of the road diet application on the corridor of New Jersey Avenue, northwest, in the city of Washington, District of Columbia. The corridor included five signalized and one unsignalized intersections. Before-and-after analyses using Synchro 11 simulation and Site-Specific Empirical Bayes analysis were used to evaluate and compare existing and proposed scenarios. The proposed scenario provided various benefits including offering accessibility to the businesses in the area and acting as a traffic calming strategy. For signalized intersections, the overall performance remained the same for most intersections except for one intersection (on P Street), as it is significantly impacted by the road diet design due to the dramatic increase of traffic volumes in its minor streets as a result of diverting traffic volumes from the unsignalized intersection for left and through movements. Results showed that the use of a road diet design enhanced the unsignalized intersection performance due to the traffic volume divergence from its minor streets and enhanced the safety of the study area by decreasing the annual number of predicted crashes. To achieve better operational benefits and reflect traffic demands, the paper recommends to re-optimize signal timings when a road diet design is adopted.
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    Joint Impact of Rain and Incidents on Traffic Stream Speeds
    Elhenawy, Mohammed; Rakha, Hesham A.; Ashqar, Huthaifa I. (Hindawi, 2021-01-11)
    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.
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    A Meso-Scale Petri Net Model to Simulate a Massive Evacuation along the Highway System
    Qabaja, Hamzeh; Ashqer, Mujahid I.; Bikdash, Marwan; Ashqar, Huthaifa I. (MDPI, 2023-03-02)
    Natural disasters may require that the residents of the affected area be evacuated immediately using a potentially damaged infrastructure. In this paper, we developed a mesoscopic simulation modeling approach for modeling traffic flow over a large geographic area and involving many people and vehicles. This study proposed a novel model, namely, Colored Deterministic and Stochastic Petri Net (CDSPN), which can mesoscopically provide an individual vehicular traffic dynamic. Each vehicle has a unique identifier, speed, distance to go, assigned target, and a specific route. It also proposed a method to automatically construct a Petri net model that represents the evacuation of Guilford County (GC), North Carolina, from standard Geographic Information Systems (GIS) shapefiles. We showed that this model could successfully simulate the dynamics of hundreds of thousands of vehicles moving on the highway system towards pre-specified safe targets such as medical facilities, exit points, or designated shelters. The vehicles are assumed to obey traffic laws, and the model reflects the complexities of the actual highway systems. The developed software can be used to analyze in reasonable detail the evacuation process, such as identifying bottlenecks and estimating efficiency and the time needed. An explicit list of 18 assumptions is stated and discussed. The Petri net for GC evacuation is reasonably massive, consisting of 35,476 places and 43,540 transitions with 531,595 colored tokens, where each token represents a vehicle in GC. We simulate the evacuation, develop statistics, and evaluate patterns of evaluation. We found that the evacuation took about 8.7 h.
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    A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems
    Elhenawy, Mohammed; Komol, Mostafizur R.; Masoud, Mahmoud; Liu, Shi Qiang; Ashqar, Huthaifa I.; Almannaa, Mohammed Hamad; Rakha, Hesham A.; Rakotonirainy, Andry (MDPI, 2021-07-06)
    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.
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    Perception Analysis of E-Scooter Riders and Non-Riders in Riyadh, Saudi Arabia: Survey Outputs
    Almannaa, Mohammed Hamad; Alsahhaf, Faisal Adnan; Ashqar, Huthaifa I.; Elhenawy, Mohammed; Masoud, Mahmoud; Rakotonirainy, Andry (MDPI, 2021-01-16)
    This study explores the feasibility of launching an e-scooter sharing system as a new micro-mobility mode, and part of the public transportation system in the city of Riyadh, Saudi Arabia. Therefore, survey was conducted in April 2020 to shed light on the perception of e-scooter systems in Riyadh. A sample of 439 respondents was collected, where majority indicated willingness to use the e-scooter sharing system if available (males are twice as likely to agree than females). Roughly 75% of the respondents indicated that open entertainment areas and shopping malls are ideal places for e-scooter sharing systems. Results indicated that people who use ride-hailing services such as Uber, expressed more willingness to use e-scooters for various purposes. The study found that the major obstacle for deploying e-scooters in Saudi Arabia is the lack of sufficient infrastructure (70%), followed by weather (63%) and safety (49%). Moreover, the study found that approximately half of the respondents believed that COVID-19 will not affect their willingness to ride e-scooters. Two types of logistic regression models were built. The outcomes of the models show that gender, age, and using ride-hailing services play an important role in respondents’ willingness to use e-scooter. Results will enable policymakers and operating agencies to evaluate the feasibility of deploying e-scooters and better manage the operation of the system as an integral and reliable part of public transportation.