Impact of traffic congestion in urban areas
This explores how self-driving cars, known as AVs, can change city traffic. Impact of traffic congestion in urban areas is a big problem for people traveling in cities. Thoughtful consideration of how AVs affect road capacity is essential. The research uses the Macroscopic Fundamental Diagram (MFD) tool to measure road capacity. The MFD is a crucial way to understand traffic flow in cities.
To study this, researchers used a computer program called SUMO. They created simulations of traffic with different numbers of autonomous vehicles. These simulations were done on two types of road networks: a made-up grid network and a real one in Budapest.
As the number of AVs increases, road capacity gets better. This means traffic can move more smoothly and more cars can use the roads effectively. Another part of the study developed a model to predict how AVs affect traffic. This model, known as the Generalized Additive Model (GAM), helps to understand the MFD with different levels of AVs.
This research shows that more AVs could lead to less impact of traffic congestion in urban areas. It also provides a new way to predict traffic flow with different amounts of AVs on the roads. This is important for planning future city traffic and making travel more accessible.
Introduction:
Traffic jams are a big problem in big cities. It also has impact of traffic congestion in urban areas. They cause delays, make travel uncomfortable, and lead to economic losses. Congestion happens when there are too many cars and not enough road space. One solution is to balance the number of vehicles (demand) with road space (supply). But building more roads is expensive and bad for the environment.
Many things affect how many cars a road can handle. This includes accidents, the design of the road, and the weather. However, we can also improve traffic by using existing roads better. Nowadays, self-driving cars are changing how we travel. They can make travel more efficient and safe without needing more roads.
The study focuses on how AVs can make better use of current roads. AVs can drive alone, with little or no help from a person. The Society of Automotive Engineers has created six levels to describe these cars based on how much the driver needs to do.
In an instant, this research focuses on how self-driving cars can help reduce traffic jams by using existing roads more effectively. This could be a key to solving city traffic problems without building more roads.
Theoretical foundations:
This part talks about two things. First, it debates a big-picture model for city traffic. Second, it explains how to use SUMO, a computer program, to get accurate traffic data. This helps in understanding how traffic flows in cities.
Macroscopic model for urban traffic:
This research looks at how autonomous vehicles impact of traffic congestion in urban areas. The study uses a computer program named SUMO to simulate traffic. They mixed regular cars with fully automated AVs in different amounts. The tests were done on a city traffic network and a made-up grid network. The aim was to see how traffic capacity changes with varying quantities of AVs. It has impact of traffic congestion in urban areas.
In traffic studies, a key concept is called the Macroscopic Fundamental Diagram (MFD). It shows the relationship between the number of cars (traffic flow), how packed the road is (vehicle concentration), and the average speed of the cars. The MFD helps us understand how traffic works at both the level of the entire network and individual road links.
This used data from SUMO to calculate the MFD. They examined how many cars passed through the network and the average number of vehicles per kilometer. This was done for both the whole network and individual road segments.
This research uses simulations to understand how AVs can change city traffic flow. It focuses on how the presence of AVs affects traffic capacity—considering both the overall traffic network and specific road sections. The goal is to see how AVs can make traffic smoother and roads more efficient.
Methodology:
This revision used a program called SUMO to see how self-driving cars affect city traffic. They tested two roads: a made-up grid network and a natural city road system. The aim was to understand how traffic changes when different mixes of regular cars and AVs exist.
The researchers set up experiments with various levels of AVs, from none 0% to all cars being autonomous 100%. They increased the amount of AVs by 20% each time. This way, they could see how traffic behaves with more or fewer AVs.
The results of these tests were analyzed using a method called the generalized additive model GAM. This helped them figure out the relationship between the average speeds of the cars (V) and how many cars there are per kilometer (vehicle density ρ). With this information, they could use the Macroscopic Fundamental Diagram (MFD) theory. This theory shows how the number of cars (traffic flow) is related to how crowded the roads are (density), especially when there are different amounts of AVs.
In short, the research was about understanding how adding more self-driving cars to the roads can change city traffic flow and road capacity. They looked at this in both theoretical and actual road conditions.
Modeling Autonomous vehicles:
In this inquiry, the team used a method similar to Lu and Tettamanti’s in 2018. They modified some settings in SUMO, a traffic simulation program, to realistically model future AVs. They used the Krauss Model, a default setting in SUMO, to understand how cars follow each other on the road. It has on impact of traffic congestion in urban areas.
The model focuses on how cars move in a line, including how they speed up, slow down, and keep a safe distance from the vehicle in front. It’s designed to make cars drive as quickly as possible while still being safe and able to stop to avoid crashes.
There are several key settings in this model:
Mingap:
This is the shortest distance a car will keep from the vehicle in front when stopped in traffic (measured in meters).
Accel:
How fast a car can speed up (acceleration, in meters per second squared).
Decel:
How quickly a car can slow down (deceleration, in meters per second squared).
Emergency Decel:
The fastest a car can stop in an emergency (in meters per second squared).
Sigma:
This measures how imperfect a driver is (ranging from 0 to 1).
Tau:
The time a driver likes to keep between their car and the one in front (in seconds).
For regular and self-driving cars, the settings for slowing down usually and in emergencies were kept the same for safety. The emergency stop setting was based on a 2007 study by Kudarauskas. Other settings like minimum gap, acceleration, and driver reaction time were taken from a 2016 study by Atkins Ltd.
Statistical evaluation approach:
This learning aims to know how different numbers of autonomous vehicles (AVs) on the roads change traffic flow and density. The examiners used simulation data to look at this.
First, they focused on how speed changes with different traffic densities. This relationship is part of the Macroscopic Fundamental Diagram (MFD), which shows how traffic flow, speed, and density relate. They didn’t assume this speed-density relationship was a straight line. Instead, they used a method that allowed for more flexibility in how traffic density affects speed.
In their model, they considered how the percentage of AVs impacts speed. If AVs make a significant difference, it shows up in the model. A key point is whether AVs help reduce the slowdown as traffic becomes denser.
They used a Generalized Additive Model (GAM) in the R programming language for their analysis. This method lets them include fixed and more flexible factors, like traffic density.
After modeling the speed-density relationship, they transformed it to understand the flow-density relationship. This shows how the number of cars (flow) relates to how packed the roads are (density). They did this for different levels of AVs on the streets.
To end with, they could find the critical traffic density and the corresponding flow. This means finding the point where the roads handle the most traffic efficiently. This method lets them see how AVs might change traffic flow at different usage levels.
Results:
The revision used a GAM (Generalized Additive Model) to analyze simulation data. They looked at results from both a grid network and a real-world network. First, they checked the GAM’s statistical factors to ensure the model was reasonable. After confirming the model was valid, they explored two main things: the highest traffic capacity and the density of cars for different levels of AV use. This meant figuring out how many vehicles could be on the road efficiently with varying amounts of AVs and how packed the streets are at these maximum capacity points.
Conclusion:
Most investigations focus on highways, and this examined impact of traffic congestion in urban areas. They used two city road layouts: a grid-like in many U.S. Cities and a more irregular European style. They tested traffic with different amounts of AVs, from none to all AVs, in simulations.
More AVs generally meant better traffic flow. In grid networks, traffic flow improved by about 16% with all AVs compared to no AVs. It also has impact of traffic congestion in urban areas. However, this was less than some previous studies expected. Those studies should have considered traffic build-up on nearby roads. Flow improved by around 25% in real-world network simulations with all AVs. Also, traffic could handle more cars before getting too congested, especially when AVs make up 40% or more of the vehicles. Overall, the study showed that more AVs can make urban traffic smoother and less prone to congestion.
