This document proposes an improved method for carpooling and scheduling using machine learning models. It discusses how carpooling and ride-sharing can help address issues related to traffic, pollution, and resource efficiency. The proposed system uses compatibility factors like departure times and personal traits to calculate optimal carpool matches between individuals. Simulation of the system at a university showed benefits for participants, the institution, and local cities through reduced traffic and emissions.

1. AN IMPROVED METHOD FOR CAR POOLING AND
SCHEDULING USING MACHINE LEARNING
MODEL

2. ABSTRACT
• Carpooling and the ride-sharing idea are currently resolving many issues
faced by modern societies. The issues regarding the overuse of oil, traffic
jams, inefficient use of time, pollution due to overuse of vehicles on the
road, and health problems.
• It is also expected that ride-sharing and carpooling will be more efficient
for autonomous vehicles because of their unmanned nature and full-fledged
autonomy. When unmanned cars will do the responsibility of carpooling
and ride-sharing or car-hailing, many issues regarding booking rides,
location sharing, payment handling, and privacy issues must be improved.

3. INTRODUCTION
• A group of cars, owned by a company or other organization, that can be used by
any of its employees.
• An autonomous vehicle is one that can drive itself from a starting point to a
predetermined destination in “autopilot” mode using various in-vehicle
technologies and sensors, including adaptive cruise control, active steering (steer by
wire), anti-lock braking systems (brake by wire), GPS navigation technology,
lasers
• Cyber-physical systems integrate sensing, computation, control and networking into
physical objects and infrastructure, connecting them to the Internet and to each
other. NSF is a leader in supporting advances in the fundamental knowledge and
tools to make cyber-physical systems a reality.

4. LITERATURE REVIEW
• Dynamic pricing in industrial Internet of Things: Blockchain application for
energy management in smart cities
• With the advent of advancements in the power sector, various new methods have
been devised to meet modern society’s electricity needs. To cope with these large
sets of electronic device’s current requirements, better energy distribution is
needed. Smart Grid (SG) facilitates energy providers to distribute electricity
efficiently to the user according to their particular requirements.
• Recent advancements enable SG to monitor, analyze, control and coordinate for the
demand and supply of electricity efficiency and energy saving. SG also allows two-
way real-time communication between utilities and customers using cloud and Fog
enabled infrastructures.

5. CONT.,
• Performance evaluation of data dissemination protocols for connected
autonomous vehicles
• An overwhelmed number of vehicles has wrecked the current system of
transportation due to rapid growth in population. Smart cities are the novel
innovation that is inevitable to curb the problems of traffic jams, unorganized
traffic, environmental pollution, and slow response rate to emergency situations.
• The intelligent transportation system (ITS) is an integral part of smart cities
allowing communications and interaction among vehicles. An autonomous vehicle
is the key element of ITS and the mass implementation of this emerging technology
is the solution to traffic problems linked to the current transportation system.

6. CONT.,
• Toward integrating vehicular clouds with IoT for smart city services
• Vehicular ad hoc networks, cloud computing, and the Internet of Things are among
the emerging technology enablers offering a wide array of new application
possibilities in smart urban spaces. These applications consist of smart building
automation systems, healthcare monitoring systems, and intelligent and connected
transportation, among others.
• The integration of IoT-based vehicular technologies will enrich services that are
eventually going to ignite the proliferation of exciting and even more advanced
technological marvels. However, depending on different requirements and design
models for networking and architecture, such integration needs the development of
newer communication architectures and frameworks.

7. CONT.,
• 5G vehicular network resource management for improving radio access
through machine learning
• The current cellular technology and vehicular networks cannot satisfy the mighty
strides of vehicular network demands. Resource management has become a
complex and challenging objective to gain expected outcomes in a vehicular
environment. The 5G cellular network promises to provide ultra-high-speed,
reduced delay, and reliable communications. The development of new technologies
such as the network function virtualization (NFV) and software defined networking
(SDN) are critical enabling technologies leveraging 5G. The SDN-based 5G
network can provide an excellent platform for autonomous vehicles because SDN
offers open programmability and flexibility for new services incorporation.

8. CONT.,
• Quality of service aware reliable task scheduling in vehicular cloud computing
• Vehicular Cloud Computing (VCC) facilitates real-time execution of many
emerging user and intelligent transportation system (ITS) applications by exploiting
under-utilized on-board computing resources available in nearby vehicles. These
applications have heterogeneous time criticality, i.e., they demand different Quality-
of-Service levels.
• In addition to that, mobility of the vehicles makes the problem of scheduling
different application tasks on the vehicular computing resources a challenging one.
In this article, we have formulated the task scheduling problem as a mixed integer
linear program (MILP) optimization that increases the computation reliability even
as reducing the job execution delay.

9. EXISTING SYSTEM
• An Autonomous car is a fully self-driving car that is self-aware and capable of making its own
decisions. A car that takes commands from humans and drives according to it, and for this a
passenger should also be available there to take control and tackle any situation that can cause
any danger.
• Mainly autonomous cars rely on sensors, actuators, complex scheduling algorithms, machine
learning systems, and powerful processors. Autonomous cars can sense their surrounding
environment, can interpret sensory information gathered by sensors, classify different objects
and navigate path bay obeying the transportation rules.
• autonomous vehicles, Vehicle-as-a-Service, and their role in reducing CO2 emissions. The
dataset used in this study gives insights into the city of Chicago’s taxi trips. The dataset
includes data about taxi trips, their respective duration, and anonym zed data about the
passengers.
• We also discuss some studies by a taxonomy that will identify the gap of an optimal incentive
mechanism that will influence users to join carpooling in autonomous cars instead of having
their vehicles.

10. DRAWBACKS
• This is less privacy
• Different time requirements
• Insurance issues
• It is Safety concerns
• Easy spread of diseases

11. PROPOSED SYSTEM
• A low average number of people per private vehicle and inappropriate road
infrastructure results in heavy traffic that wastes space, time and money for
the people involved. To optimize these resources, it is intended to promote
carpooling between people who share the same destination, for example,
colleagues at work or students at a university.
• This is UCarpooling, a matching system for commuting between people in
the same institution. UCarpooling is aimed at optimizing the number of
passengers in vehicles during routine trips to and from work or study.

12. CONT.,
• The difference with respect to other similar proposals is that UCarpooling
takes into account logistical details (place of departure, time of entry, etc.)
and personal traits (if you smoke, what genres of music you listen to, etc.)
as variables to calculate the percentage compatibility that different people
have to carry out a carpool.
• A simulation of the use of UCarpooling in a university in Asuncion,
Paraguay, yields favorable data reaching the conclusion that its adoption is
quite beneficial for the institution that adopts it, the people who use it, and
the cities where it is adopted.

13. ADVANTAGES
• It helps you save.
• It provides more convenience.
• It is a way to socialize.
• Less greenhouse gas emissions
• Carpooling can save you money.

14. CONCLUSION
• we have introduced basics of autonomous vehicles, connected autonomous
vehicles and car pooling. We propose to leverage the connected
autonomous vehicles as Vehicle-as-a-Service (VaaS).
• Furthermore using a rich dataset we have demonstrated how autonomous
cars can reduce overall road congestion and parking lot problems in urban
settings. Moreover, this can reduce the CO2 emissions thus ensuring a
better, healthy can clean approach towards urban transportation

15. REFERENCS
• [1] H. A. Khattak, K. Tehreem, A. Almogren, Z. Ameer, I. U. Din, and M.
Adnan, “Dynamic pricing in industrial Internet of Things: Blockchain
application for energy management in smart cities,” J. Inf. Secur. Appl.,
vol. 55, Dec. 2020, Art. no. 102615.
• [2] F. M. Malik, H. A. Khattak, A. Almogren, O. Bouachir, I. U. Din, and
A. Altameem, “Performance evaluation of data dissemination protocols for
connected autonomous vehicles,” IEEE Access, vol. 8, pp. 126896–
126906, 2020.

16. CONT.,
• [3] H. A. Khattak, H. Farman, B. Jan, and I. U. Din, “Toward integrating
vehicular clouds with IoT for smart city services,” IEEE Netw., vol. 33, no.
2, pp. 65–71, Mar. 2019.
• [4] S. Khan et al., “5G vehicular network resource management for
improving radio access through machine learning,” IEEE Access, vol. 8,
pp. 6792–6800, 2020.
• [5] T. Adhikary, A. K. Das, M. A. Razzaque, A. Almogren, M. Alrubaian,
and M. M. Hassan, “Quality of service aware reliable task scheduling in
vehicular cloud computing,” Mobile Netw. Appl., vol. 21, no. 3, pp. 482–
493, Jun. 2016.