Browsing by Author "Elhenawy, Mohammed"

Now showing 1 - 7 of 7
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    Bicycle Naturalistic Data Collection
    Elhenawy, Mohammed; Jahangiri, Arash; Rakha, Hesham A. (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-06-15)
    Recently, bicycling has drawn more attention as a sustainable and eco-friendly mode of transportation. Between 2000 and 2011, bicycle commuting rates in the United States rose by 80% in large bicycle friendly cities (BFCs), by 32% in non-BFCs, and overall by 47%. On the other hand, about 700 cyclists are killed and nearly 50,000 are injured annually in bicycle–motor vehicle crashes in recent years in the United States. More than 30% of cyclist fatalities in the United States from 2008 to 2012 occurred at intersections, and up to 16% of bicycle-related crashes were due to cyclists’ violations at intersections. In light of these statistics, this project focused on investigating factors that affect cyclist behavior and predicting cyclist violations at intersections. Naturalistic cycling data were used to assess the feasibility of developing cyclist violation prediction models. Mixed-effects generalized regression model is used to analyze the data and identify the significant factor affecting the probability of violations by cyclists. At signalized intersections, right turn, side traffic and opposing traffic are statistically significant factors affecting the probability of red light violation. At stop-controlled intersections, the presence of other road users, left turn, right turn and warm weather are statistically significant factors affecting the probability of violations. Violation prediction models were developed for stop-controlled intersections based on kinetic data measured as cyclists approached the intersection. Prediction error rates were 0% to 10%, depending on how far from the intersection the prediction task was conducted. An error rate of 6% was obtained when the violating cyclist was at a time-to-intersection of about 2 seconds, which is sufficient for most motor vehicle drivers to respond.
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    The COVID-19 impacts on bikeshare systems in small rural communities: Case study of bikeshare riders in Montgomery County, VA
    Almannaa, Mohammed; Woodson, Cat; Ashqar, Huthaifa; Elhenawy, Mohammed (Public Library of Science, 2022-12)
    The shared and micro-mobility industry (ride sharing and hailing, carpooling, bike and e-scooter shares) saw direct and almost immediate impacts from COVID-19 restrictions, orders and recommendations from local governments and authorities. However, the severity of that impact differed greatly depending on variables such as different government guidelines, operating policies, system resiliency, geography and user profiles. This study investigated the impacts of the pandemic regarding bike-share travel behavior in Montgomery County, VA. We used bike-usage dataset covering two small towns in Montgomery county, namely: Blacksburg and Christiansburg, including Virginia Tech campus. The dataset used covers the period of Jan 2019-Dec 2021 with more than 14,555 trips and 5,154 active users. Findings indicated that a bikeshare user's average trip distance and duration increased in 2020 (compared to 2019) from 2+ miles to 4+ and from half an hour to about an hour. While there was a slight drop in 2021, bikeshare users continued to travel farther distances and spend more time on the bikes than pre-COVID trips. When those averages were unpacked to compare weekday trips to weekend trips, a few interesting trip patterns were observed. Unsurprisingly, more trips still took place on the weekends (increasing from 2x as many trips to 4x as many trips than the weekday). These findings could help to better understand traveler's choices and behavior when encountering future pandemics.
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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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    A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems
    Elhenawy, Mohammed; Rakha, Hesham A.; Bichiou, Youssef; Masoud, Mahmoud; Glaser, Sebastien; Pinnow, Jack; Stohy, Ahmed (MDPI, 2021-12-04)
    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.
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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 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.