TY - JOUR
T1 - PRYORTIE
T2 - Public On-Demand Smart Transportation Service via a Scalable Fog-based Platform
AU - Ahmed, Naveed
AU - Baker, Thar
AU - Al Aghbari , Zaher
AU - Khedr, Ahmed
PY - 2023/4/20
Y1 - 2023/4/20
N2 - Over the past few years, the on-demand transport services (e.g., Uber) have witnessed an unprecedented popularity due to its easy-to-adopt model (i.e., a passenger orders a taxi, a request is sent to all drivers in the vicinity, drivers accept/reject the order). Nonetheless, such a service is affected directly by multiple urban factors such as traffic, number of existing drivers en-route, and local weather which may cause a huge delay. Dial-A-Ride Problem with Transfers (DARPT) allows passengers to change vehicles during their trip in a bid to minimize the delay. However, DARPT relies solely on using the existing Global Positioning System (GPS) that suffers from delay in propagating raw traffic data (e.g., route coordinates, geodesic distance, and time stamp) on time and ultimately causes traffic disruption and increases ride time constraint. To this end, this work introduces a novel interactive Fog-based navigation simulator, PRYORTIE. PRYORTIE uses roadside smart units in the form of fog nodes as a means for collecting and sharing real-time traffic data comprising of vehicles’ location and speed within the area, they each cover. These nodes can calculate an optimal route for a vehicle using a novel path recommender algorithm that utilizes a vehicle's current position, its destination, and current traffic data. The nodes can update the route in real-time using the same algorithm if traffic conditions within their radius change. A package delivery case study has been implemented and simulated in PRYORTIE to demonstrate the impact of using fog nodes in routing and navigation. Compared with the state-of-the-art, the proposed approach shows on average 46.34% improvement in saving travel time. The proposed method clearly demonstrates tangible benefits of communicating real-time data to drivers and lays the ground for subsequent future work in smart transportation and navigation especially in the context of last mile delivery. It can easily be extended to autonomous vehicles and can be easily integrated into existing transportation frameworks that employ a fog-cloud paradigm for smart transportation.
AB - Over the past few years, the on-demand transport services (e.g., Uber) have witnessed an unprecedented popularity due to its easy-to-adopt model (i.e., a passenger orders a taxi, a request is sent to all drivers in the vicinity, drivers accept/reject the order). Nonetheless, such a service is affected directly by multiple urban factors such as traffic, number of existing drivers en-route, and local weather which may cause a huge delay. Dial-A-Ride Problem with Transfers (DARPT) allows passengers to change vehicles during their trip in a bid to minimize the delay. However, DARPT relies solely on using the existing Global Positioning System (GPS) that suffers from delay in propagating raw traffic data (e.g., route coordinates, geodesic distance, and time stamp) on time and ultimately causes traffic disruption and increases ride time constraint. To this end, this work introduces a novel interactive Fog-based navigation simulator, PRYORTIE. PRYORTIE uses roadside smart units in the form of fog nodes as a means for collecting and sharing real-time traffic data comprising of vehicles’ location and speed within the area, they each cover. These nodes can calculate an optimal route for a vehicle using a novel path recommender algorithm that utilizes a vehicle's current position, its destination, and current traffic data. The nodes can update the route in real-time using the same algorithm if traffic conditions within their radius change. A package delivery case study has been implemented and simulated in PRYORTIE to demonstrate the impact of using fog nodes in routing and navigation. Compared with the state-of-the-art, the proposed approach shows on average 46.34% improvement in saving travel time. The proposed method clearly demonstrates tangible benefits of communicating real-time data to drivers and lays the ground for subsequent future work in smart transportation and navigation especially in the context of last mile delivery. It can easily be extended to autonomous vehicles and can be easily integrated into existing transportation frameworks that employ a fog-cloud paradigm for smart transportation.
KW - Efficient and smart transportation
KW - Fog-based computing
KW - Fog-cloud vehicle navigation
KW - IoT-based Smart System
KW - Traffic simulator
UR - http://www.scopus.com/inward/record.url?scp=85153570278&partnerID=8YFLogxK
U2 - 10.1016/j.iswa.2023.200228
DO - 10.1016/j.iswa.2023.200228
M3 - Article
SN - 2667-3053
VL - 18
JO - Intelligent Systems with Applications
JF - Intelligent Systems with Applications
M1 - 200228
ER -