Research Paper: IoT and Neural Models for Traffic Congestion Reduction

This research paper explores the application of IoT and neural attention models to optimize traffic congestion and reduce accident risks in metropolitan cities. The study reviews existing literature and proposes frameworks for predicting alternative and shortest routes using neural attention models. It highlights the increasing number of vehicles and the resulting congestion as a major problem, emphasizing the need for effective solutions. The research delves into the use of artificial intelligence, including neural networks and other algorithms, to improve traffic management. The paper analyzes various AI techniques, such as Artificial Neural Networks (ANN), Genetic Algorithms, and Fuzzy Logic Models, and discusses their implementation in IoT devices. It emphasizes the importance of real-time data processing and the ability of neural networks to adapt to changing inputs. The paper also examines vehicle routing problems and the use of reinforcement learning to find optimal solutions. The research design focuses on secondary data collection and qualitative analysis of existing research papers, with the aim of identifying the most effective algorithms for reducing traffic congestion and accidents. The findings suggest that the greedy selection of TSP algorithms, supported by IoT and neural attention models, is the most effective approach for optimizing traffic flow and minimizing risks. The study stresses the need for AI systems to make decisions based on real-world scenarios rather than theoretical models.

Running head: RESEARCH PAPER
Research Paper
Topic: Framework in optimizing traffic congestion and reduce accidental risk using IoT and
neural attention models for routing
Name of the Student
Name of the University
Author Note

1
Abstract
Number of vehicles are growing each day and this is why traffic congestion occurs. This is a
huge issue and mostly all kinds of engineers are facing problems due to this in metropolitan
cities. A novel literature review is extracted in this chapter to analyze the frameworks in
optimizing traffic congestion and reduce accidental risk using IoT and neural attention
models for routing. These models would help in predicting the alternative and shortest routes
to the metro cities and once road congestion is predicted, a proactive vehicle rerouting
strategy based on global distance and local pheromone is employed to assign alternative
routes to selected vehicles before they enter congested roads.

2
Introduction
One of the most problematic situations around the entire world is the issues regarding
traffic congestion. The amount of transportation congestion has increased at such a level that
the reason was being studied. It was found that the primary reason behind this has been the
rapid increase in the number of vehicles so as it has been the number of populations. Since
this has been a problematic situation for the road commuters, it has been extremely essential
that a specific and effective solution is to be found so that the problems regarding the traffic
congestions can be reduced. Inclination to the technology and ease of travelling through
public and private transports is making the individuals spent more time on roads to travel
from one place to another and this is why, there have been several studies done and
techniques developed so that there might be a solution to the current situation (Alsrehin,
Klaib & Magableh, 2019). Based on these ideas, the following research would be evaluating
the different techniques used to control the transportation with the help of smart methods to
optimize the traffic congestions and at the same time reducing the risks of accidents with the
help of IoT. Several researches also focused on neural attention models for routing for
clearing out the road congestions and reducing traffic.
Literature Review/ Defining the problem context
The detailed description in this section would include the research papers by various
researchers who have already researched about the use of Artificial Intelligence in reducing
the transport congestions and making the transport commute a better occurrence. However,
before this, it is required that there are several areas that needs to be understood before the
implementation of Artificial Intelligence can be figured out. These are the issues and
challenges that are mostly faced by the people when in comes to the frequent congestion of
the vehicles on the roads nowadays.

3
As per the author Alsrehin, Klaib and Magableh (2019), the primary issues and
challenges that people face every now and then are the increased amount of demand to travel,
and even then, via individual vehicles. The incredible rise in demand for travelling has
increased the amount of environmental degradation. Not just the environmental degradation,
but the safety concerns of human lives were also in question. This is why, the AI methods
slowly made their way into the implementations of efficient and effective solutions for the
traffic fields. These include the Artificial Neural Networks or the ANN, the Genetic
Algorithms, the Simulated Annealing, Artificial Immune System or AIS, Fuzzy Logic Model
or FLM and many others (Wang & Li, 2019). The author Wang and Li (2019), also mentions
that there are several areas in Artificial Intelligence which has a wide area of spread and
implementation that makes any machine work like a human brain. This was developed as the
age-old procedures and computational techniques cannot function in complex scenarios of the
latest times.
Studying the perspective of the different algorithms in use, analysing their impact on
the real life incidents and the ways that they are tested, and the experimentation following the
implementation of the algorithms to the IoT devices, it can be said that this model is not tried
and tested separately. The occurrence of different incidences and deriving it from a single
source with numerous combinations and permutations about hypothetical scenarios is not
found to be used in this algorithm (Conca Ridella & Sapori, 2016). this could have been an
ideal solution to the traffic condition problems with neural network routing but it does not
understand the validation of testing with several instances that the machine needs to learn and
utilise.
In another research paper, the author Alsrehin, Klaib and Magableh (2019), shares the
information that the complex problems in which the traffic congestions and road blocks are
creating issues in the latest times are beginning to have several implications such that there

4
would be a requirement for effective AI routing techniques. Implementation of an effective
AI system reflects the way a human uses his or her intellect at different situations. The same
would be imitated by a machine through several programmings to understand the complex
situation about the traffic congestions. This is why, it was suggested by the author Hussain,
Jamwal and Zaman (2015), that it would be a better implementation if the neural model
algorithms are used; for example, for finding shortest path during a travel and finding the best
routes with lesser stoppage time for private and public vehicles . These are the series of
algorithms that recognize the underlying facts behind any given situation for a car on the road
and at the same time a set of data for processing works the ways to find solution to the
problems that are being faced by the vehicles on road at real-time. The authors Abduljabbar
et al. (2019), states that the neural networks can easily adapt to the changes in the inputs so
that the network might generate the best possible results without a probable input provided or
without any requirement of redesigning the output criteria.
Another author Wang and Li (2019), points out about the vehicle routing problems
which has been prepared as an end to end framework with the use of reinforcement learning.
With this particular solution a single model is trained that would provide an optimal solution
for instant problems in a given distributive traffic area where the reward signals would be
observed and visibility rules would be followed. Outperforming vehicle routing problem was
analysed through classical heuristics and it was found that the approach was able to handle all
the problems with split delivery and also had the ability for exploring the effectivity of
variants with quality solution. However, the design was modelled on several instances that
was theoretical. The algorithm engineer for solving the real-life incidences was not yet found
to be experimented by this research procedure. The neural schema that is being aimed at for
solving the problem by an artificial intelligence system is extremely necessary for traffic
control problems as the road incidences do not oblige theoretical aspects. Number of

5
instances that this research model has been generating is over 10,000 instances at a time with
a progress overtime analysed with validation data. The coordinates are then generated
uniformly at random per unit square of optimal opportunity provided. This would help the
machine to analyse different instances and their varied outcomes. Therefore, with this
literature review, the purpose of this research was to study the different frameworks that
optimise the traffic condition for reducing accidental risk but it focuses more on the
implementation of artificial intelligence and a neural attention routing module must be given
attention to. The only purpose behind this is to make sure that the utility of all the algorithms
that has been in used in the recent times is studying enough to find out which of these
algorithms have been successfully using the artificial intelligence system and making
machine think like a real person in real life incidents is when traffic conditions and the
incidence is following these are not acting theoretically or is programmed within the
algorithm. The reaction and decision making systems to this artificial intelligence enabled
routing systems should be able to understand incidences outside the programmable theories
and take decisions effectively according to the situations.
In another paper it has been found by Nazari et al. (2018), that the nearest algorithm
for neural network policy has been used in a research where a trained model has been found
to be using the rain for system efficiently with simple and robust baseline. This is reduced the
optimality gap of a single tour construction to improve the recent reinforcement learning
framework where the variants overlooked the vehicle routing problem for having a better and
improved approach develop for travelling salesman problems (Cao et al., 2016). Thus, the
TSP algorithm provides a better outlook about the decision making system of a machine to
discard one option over another and choosing the best framework or algorithm to go along
with the neural networks of a machine.

6
Research Design/Methodology
For this particular research, mostly the research design and methodology has been
developed focusing on the secondary data collection model as a qualitative research. Simply
putting this, the neural routing system enabled by artificial intelligence in reducing the
accidental risk and traffic condition in the real life would be analysed as per the algorithm
developed by other researchers to their research reports (Conca Ridella & Sapori, 2016). Out
of all the algorithms that have been short listed to be included in this research, the analysis
with further discover which of these algorithms have been effectively utilizing artificial
intelligence system enabling a neural model for reducing the risks of accidents and traffic
conditions. This should be driven by the artificial intelligence system that would enable a
machine to take feasible decisions and find out the shortest route possible to a destination
from a source.
The secondary and primary data collection would follow through a literary conceptual
analysis, which would be appropriate for this particular research paper. The information
analysis as the form of a context analysis would be feasible to use for this research paper,
following the results found from the data collected from these research papers.
Results/ Findings
Therefore, as per the findings by different research analysts and renowned authors, it
was found that different scenarios have put together several algorithms that would be helpful
for the people on the road controlling a vehicle and also at the same time make IoT devices
work on their aid to reduce traffic congestion problems and the risks of accidents. The most
effective out of all of these have been the base recorder for TSP which find out the embedded
graphs and node embeddings. This helps in the understanding of the contacts where
reinforcement with roll out baseline is used as a self-critical training provided to the the
machines in use. This has been motivated by the estimation of an instance to be estimated by

7
their performance applied to an algorithm. This is where the higher cost on the instances has
made the researchers form a baseline for rolling out the algorithms by eliminating variances
and forcing the result to be deterministic and effective and action with maximum probability.
Conclusion
Therefore in conclusion it can be said that out of all the algorithms analyzed in this
particular research, the greedy selection of TSP algorithms supporting the optimizing traffic
congestion and reduce accidental risk using IoT and neural attention models for routing for
the is the most effective algorithms of all that would be the best in reducing accidental risk
and traffic conditions aided by neural routing schemes developed by the artificial intelligence
personnel. This is the best and the most effective model that generalize is tested on different
sizes of real life incidences and check out the probabilities of an incident and their different
occurrence and outcomes. The machine is then trained with the highest valuation of
performance about each instance size. The algorithm understands the systematic and heuristic
searching of solutions by the machine and this is why the machine is program to be
encouraging for having good results with different approaches and greedily constructing a
single solution.

8
References
Abduljabbar, R., Dia, H., Liyanage, S. and Bagloee, S.A., 2019. Applications of artificial
intelligence in transport: An overview. Sustainability, 11(1), p.189.
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of artificial
intelligence in transport: An overview. Sustainability, 11(1), 189.
Alsrehin, N.O., Klaib, A.F. and Magableh, A., 2019. Intelligent Transportation and Control
Systems Using Data Mining and Machine Learning Techniques: A Comprehensive
Study. IEEE Access, 7, pp.49830-49857.
Cao, Z., Jiang, S., Zhang, J., & Guo, H. (2016). A unified framework for vehicle rerouting
and traffic light control to reduce traffic congestion. IEEE transactions on intelligent
transportation systems, 18(7), 1958-1973.
Conca, A., Ridella, C., & Sapori, E. (2016). A risk assessment for road transportation of
dangerous goods: a routing solution. Transportation Research Procedia, 14, 2890-2899.
Hussain, M.W., Jamwal, S. and Zaman, M., 2015. Congestion control techniques in a
computer network: a survey. International Journal of Computer Applications, 111(2).
Kojić, N., Reljin, I., & Reljin, B. (2006). Neural network for optimization of routing in
communication networks. Facta universitatis-series: Electronics and Energetics, 19(2),
317-329.
Kool, W., Hoof, H. V., & Welling, M. (2018). Attention solves your TSP,
approximately. Statistics, 1050, 22.
Nazari, M., Oroojlooy, A., Snyder, L., & Takác, M. (2018). Reinforcement learning for
solving the vehicle routing problem. In Advances in Neural Information Processing

9
Systems (pp. 9839-9849).
Wang, S. and Li, Z., 2019. Exploring the mechanism of crashes with automated vehicles
using statistical modeling approaches. PloS one, 14(3).
Wang, S., & Li, Z. (2019). Exploring the mechanism of crashes with automated vehicles
using statistical modeling approaches. PloS one, 14(3).
Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine
learning and IoT in smart transportation. Future Internet, 11(4), 94.