Vehicular Routing and Intelligent Transportation Systems

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2	layout	Jessie Wu	-179 word(s)	3017	2023-11-10 04:50:47	\|

This entry is adapted from the peer-reviewed paper 10.3390/computers12040080

Urban areas all over the world, from New York's skyscraper-filled skyline to Casablanca's busy streets, have been coping with an exponential surge in vehicle traffic in recent years. This phenomena highlights the larger socioeconomic dynamics influencing current period as well as the world's rising obsession with autos. The effects of this traffic increase are being felt most acutely in emerging powerhouses and developed countries with their advanced industries and economies that are rapidly industrializing and urbanizing. A series of difficulties have arisen as a result of the growth in vehicle traffic. Cities are now frequently congested with traffic, turning once-smooth thoroughfares into figurative parking lots during rush hours. In addition to trying commuters' patience, congestion like this has real-world economic repercussions. The need for transportation increases logically as cities grow in population with younger citizens. What is particularly alarming, though, is the glaring inconsistency in many urban areas: while the number of automobiles increases, there is a glaring delay in improving road infrastructure and bolstering safety measures. The promise of effortless urban mobility is in danger of becoming an uncontrollable nightmare due to this imbalance.

intelligent decision information technology congestion prediction intelligent transportation

1. Introduction

The growth in road traffic and demand for transportation in recent years has produced major congestion, delays, accidents, and environmental concerns, particularly in large cities. Traffic congestion has become a true nuisance that affects both developed and developing countries. It impacts both automobiles and public transportation passengers, and it has numerous negative social consequences in addition to diminishing economic efficiency. The alarming part is that this modern-day manifestation has been escalating without showing any indications of slowing down, resulting in a nightmare that undermines the quality of urban living. Various factors, such as increased purchasing power of middle-income socioeconomic classes, greater availability of credits, relative price reductions, and greater supply of used vehicles, have all contributed to a rapid increase in the number of vehicles in developing countries over the last few decades. Increased individual mobility has resulted from an increased availability of vehicles, which, along with population expansion in cities, has resulted in increased congestion. With the progress of urbanization, a lot of traffic problems are appearing, especially when comparing the rising number of cars and the far lower extension of road safety in different cities around the world.

The intelligent Transportation System Market is expected to grow from USD 17.9 billion to USD 36.5 billion in 2025 ^[1]. The market growth can be attributed to several factors, including the rising number of vehicles on the road. Statistical analysis has shown that the use of vehicles, especially cars, has increased since 2006. The global use of passenger cars and commercial vehicles has seen a big growth over the period 2006–2015 ^[2]. The graph clearly shows a big growth in vehicle usage.

This problem exists globally and in Morocco as well; according to the Moroccan Ministry of Equipment, Transport and Logistics, the number of vehicles has increased from 2,791,004,000 in 2010 to 4,056,598,000 in 2017.

More specifically, in the region of Tangier-Tetouan where this research’s data were collected, the number of vehicles on the road has increased, as shown Figure 1.

Figure 1. Evolution of numbers of cars in the region of Tangier-Tetouan (2003–2013).

Traffic management methods not only affect public safety and comfort, but they can also cause poor air quality in urban environments, which may directly affect public health. Road travel is a significant contributor to air pollution in cities and towns as the emissions from vehicles on the road can have negative impacts on air quality. These emissions include a variety of harmful substances, such as carbon monoxide, nitrogen oxides, and particulate matter, which can cause a range of health problems. In particular, traffic pollution can contribute to the formation of smog, a mixture of air pollutants that can reduce visibility and cause respiratory issues.

Other modes of transportation, such as cargo ships, diesel trains, and heavy vehicles, can also contribute significantly to air pollution, particularly in areas with high levels of transportation activity. These modes of transportation rely on fossil fuels to operate. The use of fossil fuels for transportation is a major contributor to air pollution. When fossil fuels are burned, they release a variety of harmful substances into the air, including particulate matter, nitrogen oxides, and carbon dioxide.

Particulate matter, or PM, is a type of air pollution that consists of tiny particles suspended in the air. These particles can be inhaled deep into the lungs, where they can cause respiratory and cardiovascular problems. Nitrogen oxides are a group of gases that are produced when fossil fuels are burned. They contribute to the formation of smog and can cause respiratory problems. Carbon dioxide is a greenhouse gas that contributes to climate change.

In addition to their direct impacts on air quality, the emissions from these modes of transportation also contribute to greenhouse gases, which trap heat in the Earth’s atmosphere and contribute to global warming. The impact of these emissions can be particularly significant in densely populated areas, where there are more people exposed to the polluted air ^[3].

The problem is mainly caused by private cars users, and some vehicles cause more congestion than others. A bus, for example, can transport up to 50 people, whereas a passengers car, on average, can take up to 4 people only. The behaviors of passenger cars’ drivers also represent a major factor for congestion to occur: there are certain drivers that have no regard for other road users. Many drivers in some places, such as Tangier or Casablanca, strive to save a few seconds by forcing their way into junctions and blocking the passage of other automobiles, resulting in economic losses to others that are far higher than their own gains.

Knowing the impact that traffic congestion has on our lives, it becomes more important to find a solution. With the latest achievements in information technologies, including the Internet of Things (IoT), machine learning, and edge computing and networking, the intelligent management of traffic congestion has become a growing area of research ^[4]. Therefore, the research focused mainly on passenger cars and how to identify congestion caused by this category of drivers, and the goal is to propose a solution to lower traffic congestion levels.

Traffic in most Moroccan cities is highly congested due to many reasons, including infrastructure, number of vehicles, and the low quality and capacity of public transportation. According to the Ministry of Equipment, Transport and Logistics, traffic congestion has an impact on Moroccan economic growth and represents a challenge in terms of social and sustainable development.

Traffic congestion has major impact on public health as road noise has been shown to increase the short-term risk of death from specific diseases of the cardiovascular, respiratory, and hormonal systems. People who live in areas with a higher level of congestion and face a lot of traffic stress have a lower health status and higher depressive symptoms than people living in areas with less traffic congestion ^[5].

2. Vehicular Routing from a Variety of Perspectives

Optimal routing of vehicles, also known as vehicular routing or fleet management, refers to the process of determining the most efficient routes and schedules for a fleet of vehicles to follow in order to transport goods or passengers from one location to another. This can involve minimizing the total distance traveled, minimizing the total time spent on the road, or maximizing the number of deliveries or passengers that can be transported within a given timeframe.

In the past, researchers have tackled the problem of vehicular routing from a variety of perspectives, including mathematical optimization, artificial intelligence, and control theory.

One early approach to vehicular routing is to use mathematical optimization techniques to find the shortest or quickest route for a single vehicle to follow. This could be accomplished using algorithms, such as the Dijkstra’s algorithm or the A* search algorithm, which are commonly used to find the shortest path between two points in a graph.

To address the issues related to traffic congestion due to multiple vehicles on the road, researchers have proposed a variety of more sophisticated methods for optimizing the routes and schedules of a fleet of vehicles. For example, some approaches have used mixed-integer programming or linear programming to find the optimal routes and schedules for a fleet of vehicles, while taking into account the specific constraints and requirements of each vehicle and each delivery ^[6]. Other researchers have used machine learning techniques, such as neural networks or genetic algorithms, to learn from past data and adaptively improve the efficiency of the vehicular routing process over time.

In addition to these mathematical and computational approaches, researchers have also explored the use of control theory and feedback control to optimize the navigation of vehicles in real time. This can involve using sensors to gather data about the current state of vehicles and their surrounding environment, and then using these data to adjust the routes and schedules of the vehicles in real time in order to respond to changing conditions or unexpected events.

There are various approaches that can be taken based on artificial intelligence (AI) to solve traffic congestion problems in urban areas (Table 1).

Table 1. Novel approaches to traffic congestion mitigation in smart cities.

Paper Title and Reference	Key Points
A multi-criteria, multi-commodity flow model for analyzing transportation networks ^[7]	This work proposes a new mathematical model called the MCMCNF model for assessing various types of transportation systems. The model allows the analysis of the effects of expansions, tolls, congestion levels, and accidents on a transportation network’s resilience and vulnerability, emissions distribution, and risk. It employs multiple objectives to maximize the flow of commodities and minimize travel-related costs.
Real-time prediction and navigation to mitigate traffic congestion based on a model with equilibrium Markov chain ^[8]	This work discusses a method for mitigating traffic congestion in busy and crowded cities by using advanced prediction and navigation models on a dynamic traffic network. It divides real-time GPS data from taxis in Shenzhen city into 50 regions and uses an equilibrium Markov chain model to dispatch the vehicles in an effort to alleviate congestion. The results of the field experiments suggest that this method can effectively and efficiently reduce traffic congestion in city traffic networks while maintaining system performance.
Enhancing intelligence in traffic management systems to aid in vehicle traffic congestion problems in smart cities ^[9]	This work discusses a solution for maximizing the flow of vehicles in large cities through the use of inter-vehicle communication and AI methods. It proposes TRAFFIC, which uses an ensemble of classifiers to estimate the congestion level in a transport system, and a dissemination mechanism to propagate this information between vehicles. It claims that this approach has advanced the state of the art by achieving a higher success rate in estimating traffic congestion; reducing travel time, fuel consumption, and CO2 emissions; and providing a high coverage rate with a low packet transmission rate.
AI-powered IoT for traffic management ^[10]	This work conducted research that combines both IoT and AI to more effectively manage traffic through the voluminous data collected and generated by vehicles and other devices. It proposes a system that can be combined with information from traffic lights to minimize traffic congestions, bring greater efficiency to the road system in the city, and reduce pollution.
Advanced traffic congestion early warning system based on traffic flow forecasting and extenics evaluation ^[11]	This work proposes a traffic congestion early warning system that uses machine learning and statistical analysis to accurately forecast traffic flow parameters and predict the level of traffic congestion. The system consists of four components: point forecasting, characteristic estimation, interval prediction, and comprehensive assessment. It uses extenics evaluation theory to evaluate traffic congestion level.
Traffic monitoring system for optimizing road traffic flow ^[12]	Several traffic congestion monitoring and management systems based on the Internet of Things have been proposed in the literature. The design of a modern traffic monitoring system can optimize road traffic flow to meet current and future road traffic needs.
Pheromone-based traffic management framework ^[13]	This work proposes a new pheromone-based traffic management framework to reduce traffic congestion and unify dynamic vehicle diversion and traffic light control strategies. It presents a scheme that uses WSN to control traffic signal roads, and intelligent traffic controllers are used for traffic infrastructure operations. The simulation results prove the rationality of the scheme, solve the traffic congestion problem of the average waiting time and average queue length at a single intersection, and effectively control the global traffic flow at multiple intersections.
Smart traffic management system ^[14]	This work proposes a model for a smart traffic management system that aims to improve traffic flow and reduce congestion in smart cities. It utilizes real-time traffic data and intelligent algorithms to optimize traffic flow, reduce travel time, and minimize congestion. It incorporates various data sources, including video cameras, sensors, and social media, to provide comprehensive and accurate traffic information.

The following is a list of some possible solutions:

Traffic prediction and optimization: Machine learning algorithms, such as neural networks or support vector machines, can analyze traffic data from sensors, cameras, and other sources to predict traffic congestion and optimize traffic flow. These algorithms can learn patterns in the data and make predictions about future traffic conditions.
Intelligent transportation systems (ITS): Advanced technologies, such as sensors, cameras, and communication networks, can be used to develop intelligent transportation systems (ITS) that improve the efficiency and safety of transportation. For example, traffic management systems can optimize traffic flow and reduce congestion, and autonomous vehicles can navigate roads and avoid collisions.
Demand-based pricing: Real-time traffic data can be analyzed to adjust the price of transportation services based on demand, thereby encouraging the use of alternative modes of transportation or shifting demand to off-peak times.
Public transit optimization: Optimization techniques, such as machine learning or statistical modeling, can analyze data based on rider demand, route performance, and vehicle utilization to improve the efficiency of public transit systems. These techniques can identify patterns in the data and suggest changes to routes or schedules to reduce congestion and improve overall performance.

Artificial intelligence needs a lot of data to achieve its potential results, which is why a lot of startups are collecting data by connecting hundreds of sensors at traffic lights to obtain a general idea on why congestion is happening and how to manage it in real time.

For instance, Rapid Flow Technologies is testing its Surtrac traffic management system in the East Liberty neighborhood in Pittsburgh, USA. Rapid Flow’s technology deployed at intersections allows coordination among all the lights where it has been installed, for example, allowing a light to stay green longer to clear traffic at a particular intersection. “We have communication between intersections”, said Mr. Barlow, the co-founder and CTO of Rapid Flow Technologies ^[15]. The Surtrac system has reduced waiting times at traffic lights in the area by as much as 42%, according to Mr. Barlow. This not only gets people to their destinations quicker, but it also helps reduce auto emissions because cars are spending less time on the road.

A startup called Vivacity Labs ^[16] is taking a different approach in the town of Milton Keynes, England. It is focusing on gathering data on traffic patterns with custom-made sensors installed at traffic lights throughout the town, with the aim of eventually using the system to provide predictive traffic information and guidance to drivers. In the near future, controlling traffic lights is expected to come into play. These sensors will not simply gather information; each will be a powerful computer attached to a camera and capable of analyzing the traffic it can see at its intersection.

Currently, Vivacity uses its sensors at intersections to gather traffic information that is continually sent back to a central computer. The systemwide data can be analyzed not only to recognize current traffic conditions but also to predict how traffic patterns will develop ^[17].

Waze is a powerful tool for real-time traffic information and management. Its strength lies in its user-generated content as drivers who use the Waze app share information about traffic, accidents, and road conditions. The data are analyzed by Waze in real time to provide the most optimal route to drivers 24 h a day. While Waze is a valuable resource, it does have limitations, such as its inability to predict road conditions in advance ^[18].

Traffic congestion has a major impact on public health as road noise has been shown to increase the short-term risk of death from specific diseases of the cardiovascular, respiratory, and hormonal systems. People who live in areas with a higher level of congestion and face a lot of traffic stress have a lower health status and higher depressive symptoms than people living in areas with less traffic congestion ^[19].

This research is conducted to make drivers’ daily life easier and to participate in the well-being of urban population (both drivers and people living in areas with higher congestion levels). This research started with Tangier city, was expanded to Casablanca, and will be expanded to other cities in the future.

This research has resulted in the development of an algorithm that is capable of predicting road traffic condition based on the data collected, while also able to predict congestion in areas where no data have been collected. A mobile application for users was developed with a simple UI in order to give them the shortest and most convenient route to take for their movement inside urban areas.

The power of researchers' application and the added value are the ability to not only share real-time information but also generate maps and predict road conditions in the future based on data collected on different time frames. The application is applied offline, and once a user goes online, the data are updated with new one. Users can choose their destination and check on road state in real time or even check on congestion level in the future as the Ezzi-traffic application offers the possibility of predicting any chosen timeslot of the day and on different locations. Eventually, it should be able to direct drivers based not only on how busy the road is now, or how busy it has been a few minutes ago, but how busy it will be when they get there.

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1. Introduction

2. Vehicular Routing from a Variety of Perspectives

References