Author: 林亦琴Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Simulation and Analysis of Traffic Flow and the Influence of Automated Vehicles on Performance of Signalized Intersections
Author: 林亦琴Simulation of the Impact of Connected and Automated Vehicles at a Signalized Intersection
Author: Hamad Bader AlmobayedhPublisher:
ISBN:
Category : Autonomous vehicles
Languages : en
Pages : 118
Book Description
Intersections are locations with higher likelihood of crash occurences and sources of traffic congestion as they act as bottlenecks compared with other parts of the roadway networks. Consequently, connected and automated vehicles (CAVs) can help to improve the efficiency of the roadways by reducing traffic congestion and traffic delays. Since CAVs are expected to take control from drivers (human control) in making many important decisions, thus they are expected to minimize driver (human) errors in driving tasks. Therefore, CAVs potential benefits of eliminating driver error include an increase in safety (crash reduction), smooth vehicle flow to reduce emissions, and reduce congestion in all roadway networks. Since CAV implementations are currently in early stages, researchers have found that the use of traffic modeling and simulation can assist decision makers by quantifying the impact of increasing levels of CAVs, helping to identify the effect this will have on future transportation facilities. The main objective of the current study was to simulate the potential impacts CAVs may have on traffic flow and delay at a typical urban signalized intersection. Essentially, to use a microscopic traffic simulation software to test future CAV technology within a virtual environment, by testing different levels of CAVs with their associated behaviors across several scenarios simulated. This study tested and simulated the impact of CAVs compared with conventional vehicles at a signalized intersection. Specifically, I analyzed and compared the operations of the signalized intersection when there are only conventional vehicles, conventional vehicles mixed with CAVs, and when there are only CAVs. The most current PTV Vissim 11 software was used for simulating different percentages of three different types of CAVs and conventional vehicles in the traffic stream at the intersection. These are three different levels of automated vehicles that are already installed in PTV Vissim 11, which are AV cautious, AV normal, and AV all-knowing. All these automated vehicles were tested in different scenarios in this study. Real data from an existing signalized intersection in the city of Dayton, Ohio were used in the PTV Vissim software simulation. The traffic count data used in the Vissim intersection model were for morning peak hour. The existing signal timing data for the intersection used were first optimized using Synchro. The results from Vissim simulation show that CAVs could reduce the queue delay by about 12%, the stopped delay by about 17%, the vehicle travel time by about 17%, and the queue length by about 22%. Because of that, CAVs can substantially reduce congestion at urban signalized intersections.
Laminar Traffic Flow
Author: Nir StudnitskiPublisher:
ISBN:
Category :
Languages : en
Pages : 37
Book Description
This Computer Science Masters Thesis sets out to explore an algorithmic approach to traffic regulation of automated vehicles in a single intersection. For this purpose, an X-shaped intersection with randomized oncoming traffic is simulated using Unity 3D in C#. The Laminar Traffic Flow algorithm (LTF), written for the purposes of this thesis, is then used to modulate each arriving vehicle's speed in a way that would allow it - regardless of its size, speed, or turning plan - to drive through the intersection without stopping or colliding with other vehicles. Analysis of LTF's performance when compared to a simulation of a standard, traffic-lights-regulated intersection, shows LTF to have the following advantages: Higher throughput: Up to 20% more vehicles per second go through the intersection. Lower through-time: Vehicles going through the intersection do not need to stop, and maintain their average speed to give minimal through-time, as if the intersection is empty. The LTF algorithm did however demonstrate the disadvantage of having a failure point, a certain amount of incoming traffic load past which it cannot find a solution. How this failure point can be avoided is discussed in the conclusion (Chapter 7). With recent and future advances in traffic automation, the LTF algorithm may be of use in many areas where traffic needs to be regulated, such as intersections, railway intersections, robotic warehouses, and drone control.
Computational Logistics
Author: Tolga BektaşPublisher: Springer
ISBN: 3319684965
Category : Computers
Languages : en
Pages : 597
Book Description
This book constitutes the refereed proceedings of the 8th InternationalConference on Computational Logistics, ICCL 2017, held in Southampton,UK, in October 2017.The 38 papers presented in this volume were carefully reviewed and selected for inclusion in the book. They are organized in topical sections entitled: vehicle routing and scheduling; maritime logistics;synchromodal transportation; and transportation, logistics and supply chain planning.
Using Agent-based Vehicle Traffic Models to Analyze Traffic Flow
Author: Michael DuffPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This thesis discusses the creation of an agent-based model for vehicle traffic that uses a previously developed mathematical car-following micro-simulation model. The agent-based model results are verified against the original model. The agent-based model is then used to explore the effects of inclement weather and autonomous drivers on traffic to explore the suitability of using the agent-based approach to model traffic. The primary environment focused on is a signalized four-way intersection, with an extension to freeways for autonomous vehicle simulation. The model is used to demonstrate how optimizing green light intervals at intersections based on current weather conditions can help to partially restore traffic throughput to normal condition levels. Results from simulations with autonomous vehicles demonstrate that traffic flow steadily increases as these vehicles enter the driving population. In the case of signalized intersections this improvement increases in inclement weather, but increases are flat across different weather conditions for freeways.
Observational Before-after Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety
Author: Ezra HauerTraffic Operations Assessment
Author: Andalib ShamsPublisher:
ISBN: 9780438386204
Category : Automobiles
Languages : en
Pages : 76
Book Description
As traffic congestion increases day by day, it becomes necessary to improve the existing roadway facilities to maintain satisfactory operational and safety performances. Moreover, Deployment of Connected and Autonomous Vehicles (CAV) will increase roadway capacity, but their induced demand may lead to further congestion. Increasing roadway capacity can reduce traffic congestion up to a certain extent, but it can be very costly and sometimes conventional methods are not suitable enough. Using innovative intersection designs, such as the Continuous Flow Intersection (CFI), instead of conventional four-legged intersections, have proven to be beneficial in increasing capacity and reducing congestion. Public transit systems that run in mixed traffic also experience increased travel times and reduction in reliability due to the increased levels of congestion. Implementing transit preferential treatments, often in conjunction with rapid transit modes, is a proven way to improve transit operations along congested corridors. This study focuses on assessing future traffic and transit conditions in year 2040, and potential improvement alternatives along sections of Redwood Road in Salt Lake and Utah Counties, Utah through VISSIM traffic simulation. In addition to the models of existing conditions, five scenarios were developed for 2040: Do-Nothing, Street Widening, implementation of a CFI, Transit Exclusive Lanes, and implementation of Transit Signal Priority (TSP) in conjunction with exclusive lanes. Among the developed scenarios, CFI scenario have been implemented only at the intersection of 9000 S and Redwood Road. Results suggests that, without any improvement, it would be impossible to maintain a satisfactory level of performance in 2040. In the street widening scenario number of lane have been updated to four. This street widening scenario is a possible improvement option, but still underperforms along certain segments and intersections. The conventional four-legged intersection of Redwood Road and 9000 S was replaced with a CFI, which helped in reducing the total delay for both passenger cars and transit. In the Transit Exclusive Lane scenario a lane have been added over the street widening scenario exclusively for transits. So, in this scenario four lanes are for vehicles and one lane is dedicated for transits. Transit exclusive lanes reduced the total intersection transit delay by 20% compared to Do-Nothing. The Transit Signal Priority scenario have been included over the Transit Exclusive Lane scenario. Combining TSP with transit exclusive lanes resulted in a 61% reduction in transit delays, while the vehicular traffic along the corridor also benefited from it. The cross street traffic mostly benefited from street widening, while it experienced some impact with TSP, although it was not statistically significant. TSP also performed well at the introduced CFI, where transit experienced 42% reduction in delay, with an improved performance for vehicular traffic compared to Do-Nothing. In the recent years, improvements in vehicular technology has been significant. Even after this improvement, right now it is only a fraction of what is being expected in the future. Vehicles in the future will be able to sense its environment and navigate the surroundings without any sort of human input. Moreover, vehicles will be able to communicate with other vehicles, infrastructures, pedestrians, and the cloud. These vehicles are introduced as Connected and Autonomous Vehicles. Driving behavior of these vehicles will be different than conventional vehicles. With the help of automation, these vehicles will have a shorter headway, faster perception-reaction time and more uniform speed than conventional vehicles. Using connected technology, vehicles will be able to form platoons and optimize their speed profile and routing decisions. Though it is known that CAV will act more cooperatively than conventional vehicles, there is little development in the improvement of driving behaviors or intersection control strategies to make them more cooperative. Considering these issues, this study developed signalized intersection control strategy algorithm based on TSP and tested the performance of the Intelligent Driver Model which does not consider the human-reaction time along with the developed algorithm. For the developed algorithms it has been assumed that vehicles are fully connected and the automation level is at least four. Alternative scenarios have been developed over the 2040 Do-Nothing scenario with 25%, 50%, 75% and 100% CAV penetration. CAV’s performance has also been assessed in comparison with the Transit Signal Priority scenario which includes all the traditional and innovative improvement strategies implemented in this study. Results suggest that travel delay at intersections and travel time at road segment would decrease with the increase in CAV penetration. Overall network delay and travel time would also decrease with increased CAV penetration. Though initially number of stops increased and average speed decreased, with more penetration both of the parameter performs better.
Safety Data, Analysis, and Modeling
Author: Publisher:
ISBN: 9780309125956
Category : Roads
Languages : en
Pages : 198
Book Description
TRB's Transportation Research Record: Journal of the Transportation Research Board, No. 2083 includes 22 papers that explore data-driven perspective on safety risk management, macrolevel annual safety performance measures, tool with road-level crash prediction for safety planning, congestion and number of lanes on urban freeways relationship to safety, accident modification factors, identifying hazardous road locations, identifying hot spots, and safety influence area for four-legged signalized intersections. This issue of the TRR also examines automated analysis of accident exposure, new simulation-based surrogate safety measure, hit-and-run crashes, speed limit increases' effect on injury severity, safety of curbs, proximity to intersections and injury severity of urban arterial crashes, nested logit model of traffic flow on freeway ramps, intelligent transportation system data for assessing freeway safety, vehicle time spent in following on two-lane rural roads, indirect associations in crash data, crash prediction models for rural highways, and methodology for identifying causal factors of accident severity.
Simulation and Queueing Network Model Formulation of Mixed Automated and Non-automated Traffic in Urban Settings
Author: Nathaniel Karl BaileyPublisher:
ISBN:
Category :
Languages : en
Pages : 43
Book Description
Automated driving is an emerging technology in the automotive industry which will likely lead to significant changes in transportation systems. As automated driving technology is still in early stages of implementation in vehicles, it is important yet difficult to understand the nature of these changes. Previous research indicates that autonomous vehicles offer numerous benefits to highway traffic, but their impact on traffic in urban scenarios with mixed autonomous and non-autonomous traffic is less understood. This research addresses this issue by using microscopic traffic simulation to develop understanding of how traffic dynamics change as autonomous vehicle penetration rate varies. Manually driven and autonomous vehicles are modeled in a simulation environment with different behavioral models obtained from the literature. Mixed traffic is simulated in a simple network featuring traffic flowing through an isolated signalized intersection. The green phase length, autonomous vehicle penetration rate, and demand rate are varied. We observe an increase in network capacity and a decrease in average delay as autonomous vehicle penetration rate is increased. Using the results of the simulation experiments, an existing analytical network queueing model is formulated to model mixed autonomous and non-autonomous urban traffic. Results from the analytical model are compared to those from simulation in the small network and the Lausanne city network, and they are found to be consistent.
Adaptive Traffic Signal Control Using Deep Reinforcement Learning for Network Traffic Incidents
Author: Tianxin Li (M.S. in Engineering)Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Traffic signal control is an essential aspect of urban mobility that significantly impacts the efficiency and safety of transportation networks. Traditional traffic signal control systems rely on fixed-time or actuated signal timings, which may not adapt to the dynamic traffic demands and congestion patterns. Therefore, researchers and practitioners have increasingly turned to reinforcement learning (RL) techniques as a promising approach to improve the performance of traffic signal control. This dissertation investigates the application of RL algorithms to traffic signal control, aiming to optimize traffic flow and reduce congestion. The study develops a simulation model of a signalized intersection and trains RL agents to learn how to adjust signal timings based on real-time traffic conditions. The RL agents are designed to learn from experience and adapt to changing traffic patterns, thereby improving the efficiency of traffic flow, even for scenarios in which traffic incidents occur in the network. In this dissertation, the potential benefits of using RL algorithms to optimize traffic signal control in scenarios with and without traffic incidents were explored. To achieve this, an incident generation module was developed using the open-source traffic signal performance simulation framework that relies on the SUMO software. This module includes emergency response vehicles to mimic the realistic impact of traffic incidents and generates incidents randomly in the network. By exposing the RL agent to this environment, it can learn from the experience and optimize traffic signal control to reduce system delay. The study began with a single intersection scenario, where the DQN algorithm was modeled to form the RL agent traffic signal controller. To improve the training process and model performance, experience replay and target network were implemented to solve the limitations of DQN. Hyperparameter tuning was conducted to find the best parameter combination for the training process, and the results showed that DQN outperformed other controllers in terms of the system-wise and intersection-wise queue distribution and vehicle delay. The study was then extended to a small corridor with 2 intersections and a grid network (2x2 intersection), and the incident generation module was used to expose the RL agent to different traffic scenarios. Again, hyperparameter tuning was conducted, and the DQN model outperformed other controllers in terms of reducing congestion and improving the system performance. The robustness of the DQN performance was also tested with different demands, and the microsimulation results showed that the DQN performance was consistent. Overall, this study highlights the potential of RL algorithms to optimize traffic signal control in scenarios with and without traffic incidents. The incident generation module developed in this study provides a realistic environment for the RL agent to learn and adapt, leading to improved system performance and reduced congestion. In addition, hyperparameter tuning is essential to lay down a solid foundation for the RL training process