Scholarly Works, Virginia Tech Transportation Institute

Permanent URI for this collection

https://hdl.handle.net/10919/89575

Browse

Browsing Scholarly Works, Virginia Tech Transportation Institute by Author "Ashqar, Huthaifa I."

Now showing 1 - 2 of 2
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.