60 research outputs found
Mathematical Model and Cloud Computing of Road Network Operations under Non-Recurrent Events
Optimal traffic control under incident-driven congestion is crucial for road safety and maintaining network performance. Over the last decade, prediction and simulation of road traffic play important roles in network operation. This dissertation focuses on development of a machine learning-based prediction model, a stochastic cell transmission model (CTM), and an optimisation model. Numerical studies were performed to evaluate the proposed models. The results indicate that proposed models are helpful for road management during road incidents
Understanding Micro-Level Lane Change and Lane Keeping Driving Decisions: Harnessing Big Data Streams from Instrumented Vehicles
It is important to get a deeper understanding of instantaneous driving behaviors, especially aggressive and extreme driving behaviors such as hard acceleration, as they endanger traffic efficiency and safety by creating unstable flows and dangerous situations. The aim of the dissertation is to understand micro-level instantaneous driving decisions related to lateral movements such as lane change or lane keeping events on various roadway types. The impacts of these movements are fundamental to microscopic traffic flow and safety. Sufficient geo-referenced data collected from connected vehicles enables analysis of these driving decisions. The “Big Data” cover vehicle trajectories, reported at 10 Hz frequency, and driving situations, which make it possible to establish a framework.The dissertation conducts several key analyses by applying advanced statistical modeling and data mining techniques. First, the dissertation proposes an innovative methodology for identifying normal and extreme lane change events by analyzing the lane-based vehicle positions, e.g., sharp changes in distance of vehicle centerline relative to the lane boundaries, and vehicle motions captured by the distributions of instantaneous lateral acceleration and speed. Second, since surrounding driving behavior influences instantaneous lane keeping behaviors, the dissertation investigates correlations between different driving situations and lateral shifting volatility, which quantifies the variability in instantaneous lateral displacements. Third, the dissertation analyzes the “Gossip effect” which captures the peer influence of surrounding vehicles on the instantaneous driving decisions of subject vehicles at micro-level. Lastly, the dissertation explores correlations between lane change crash propensity or injury severity and driving volatility, which quantifies the fluctuation variability in instantaneous driving decisions.The research findings contribute to the ongoing theoretical and policy debates regarding the effects of instantaneous driving movements. The main contributions of this dissertation are: 1) Quantification of instantaneous driving decisions with regard to two aspects: vehicle motions (e.g., lateral and longitudinal acceleration, and vehicle speed) and lateral displacement; 2) Extraction of critical information embedded in large-scale trajectory data; and 3) An understanding of the correlations between lane change outcomes and instantaneous lateral driving decisions
Empirical study of the effect of offramp queues on freeway mainline traffic flow
The dissertation examines the relationship between the number of lane changes, the speed of the ramp lane, and the location upstream of the ramp split. Analyses indicate the number of lane changes exhibits a parabolic relationship with respect to the ramp lane speed, and the number of lane changes exhibits gamma-distributed relationship with respect to the distance upstream of the ramp. The macroscopic lane changing model presented is best characterized as the development of generalized lane-changing relationships, and provides a starting point from which more complex corridor-level models can be developed. This study also identifies an unusual car-following behavior exhibited by certain lane-changing drivers. When the target lane is moving slowly, some lane-changing drivers will slow down, causing a disruption in their initial lane. Regression analysis is used to estimate the speed upstream of the initial lane to indicate the disruption is responsible for the lateral propagation of congestion. The lane choice of exiting vehicles is also studied. Lane choice appears to be a function of origin/destination, and freeway speed. As speeds in the general purpose lanes decrease, exiting vehicles are more likely to wait longer to move into the exit ramp lanes, resulting in an increased lane changing density.
Results from this study are expected to have the greatest impact on microscopic lane-change model validation. Additionally, results have implications for design and safety issues associated with freeway ramps. As data collection technologies improve and data becomes increasingly available, this research provides the basis for the further development of more elaborate lane-changing models.Ph.D
Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022
The 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS) was held in Dresden, Germany, from November 30th to December 2nd, 2022. Organized by the Chair of Traffic Process Automation (VPA) at the “Friedrich List” Faculty of Transport and Traffic Sciences of the TU Dresden, the proceedings of this conference are published as volume 9 in the Chair’s publication series “Verkehrstelematik” and contain a large part of the presented conference extended abstracts.
The focus of the MFTS conference 2022 was cooperative management of multimodal transport and reflected the vision of the professorship to be an internationally recognized group in ITS research and education with the goal of optimizing the operation of multimodal transport systems.
In 14 MFTS sessions, current topics in demand and traffic management, traffic control in conventional, connected and automated transport, connected and autonomous vehicles, traffic flow modeling and simulation, new and shared mobility systems, digitization, and user behavior and safety were discussed. In addition, special sessions were organized, for example on “Human aspects in traffic modeling and simulation” and “Lesson learned from Covid19 pandemic”, whose descriptions and analyses are also included in these proceedings.:1 Connected and Automated Vehicles
1.1 Traffic-based Control of Truck Platoons on Freeways
1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic
1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations
1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency?
1.5 GLOSA System with Uncertain Green and Red Signal Phases
2 New Mobility Systems
2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks
2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network
3 Traffic Flow and Simulation
3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory
3.2 A RoundD-like Roundabout Scenario in CARLA Simulator
3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study
3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions
3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads
4 Traffic Control in Conventional Traffic
4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics
4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control
4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation
4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority
4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority
4.6 Towards Efficient Incident Detection in Real-time Traffic Management
4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control
5 Traffic Control with Autonomous Vehicles
5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles
5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration
6 User Behaviour and Safety
6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections
7 Demand and Traffic Management
7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data
7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices
8 Workshops
8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility
8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the FutureDas 4. Symposium zum Management zukünftiger Autobahn- und Stadtverkehrssysteme (MFTS) fand vom 30. November bis 2. Dezember 2022 in Dresden statt und wurde vom Lehrstuhl für Verkehrsprozessautomatisierung (VPA) an der Fakultät Verkehrswissenschaften„Friedrich List“ der TU Dresden organisiert. Der Tagungsband erscheint als Band 9 in der Schriftenreihe „Verkehrstelematik“ des Lehrstuhls und enthält einen Großteil der vorgestellten Extended-Abstracts des Symposiums.
Der Schwerpunkt des MFTS-Symposiums 2022 lag auf dem kooperativen Management multimodalen Verkehrs und spiegelte die Vision der Professur wider, eine international anerkannte Gruppe in der ITS-Forschung und -Ausbildung mit dem Ziel der Optimierung des Betriebs multimodaler Transportsysteme zu sein.
In 14 MFTS-Sitzungen wurden aktuelle Themen aus den Bereichen Nachfrage- und Verkehrsmanagement, Verkehrssteuerung im konventionellen, vernetzten und automatisierten Verkehr, vernetzte und autonome Fahrzeuge, Verkehrsflussmodellierung und -simulation, neue und geteilte Mobilitätssysteme, Digitalisierung sowie Nutzerverhalten und Sicherheit diskutiert. Darüber hinaus wurden Sondersitzungen organisiert, beispielsweise zu „Menschlichen Aspekten bei der Verkehrsmodellierung und -simulation“ und „Lektionen aus der Covid-19-Pandemie“, deren Beschreibungen und Analysen ebenfalls in diesen Tagungsband einfließen.:1 Connected and Automated Vehicles
1.1 Traffic-based Control of Truck Platoons on Freeways
1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic
1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations
1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency?
1.5 GLOSA System with Uncertain Green and Red Signal Phases
2 New Mobility Systems
2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks
2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network
3 Traffic Flow and Simulation
3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory
3.2 A RoundD-like Roundabout Scenario in CARLA Simulator
3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study
3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions
3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads
4 Traffic Control in Conventional Traffic
4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics
4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control
4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation
4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority
4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority
4.6 Towards Efficient Incident Detection in Real-time Traffic Management
4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control
5 Traffic Control with Autonomous Vehicles
5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles
5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration
6 User Behaviour and Safety
6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections
7 Demand and Traffic Management
7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data
7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices
8 Workshops
8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility
8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the Futur
Driver Behavior in Traffic
DTFH61-09-H-00007Existing traffic analysis and management tools do not model the ability of drivers to recognize their environment and respond to it with behaviors that vary according to the encountered driving situation. The small body of literature on characterizing drivers behavior is typically limited to specific locations (i.e., by collecting data on specific intersections or freeway sections) and is very narrow in scope. This report documented the research performed to model driver behavior in traffic under naturalistic driving data. The research resulted in the development of hybrid car-following model. In addition, a neuro-fuzzy reinforcement learning, an agent-based artificial intelligence machine-learning technique, was used to model driving behavior. The naturalistic driving database was used to train and validate driver agents. The proposed methodology simulated events from different drivers and proved behavior heterogeneities. Robust agent activation techniques were also developed using discriminant analysis. The developed agents were implemented in VISSIM simulation platform and were evaluated by comparing the behavior of vehicles with and without agent activation. The results showed very close resemblance of the behavior of agents and driver data. Prototype agents prototype (spreadsheets and codes) were developed. Future research recommendations include training agents using more data to cover a wider region in the Wiedemann regime space, and sensitivity analysis of agent training parameters
Improving Safety under Reduced Visibility Based on Multiple Countermeasures and Approaches including Connected Vehicles
The effect of low visibility on both crash occurrence and severity is a major concern in the traffic safety field. Different approaches were utilized in this research to analyze the effects of fog on traffic safety and evaluate the effectiveness of different fog countermeasures. First, a Crash Risk Increase Indicator (CRII) was proposed to explore the differences of crash risk between fog and clear conditions. A binary logistic regression model was applied to link the increase of crash risk with traffic flow characteristics. Second, a new algorithm was proposed to evaluate the rear-end crash risk under fog conditions. Logistic and negative binomial models were estimated in order to explore the relationship between the potential of rear-end crashes and the reduced visibility together with other traffic parameters. Moreover, the effectiveness of real-time fog warning systems was assessed by quantifying and characterizing drivers\u27 speed adjustments through driving simulator experiments. A hierarchical assessment concept was suggested to explore the drivers\u27 speed adjustment maneuvers. Two linear regression models and one hurdle beta regression model were estimated for the indexes. Also, another driving simulator experiment was conducted to explore the effectiveness of Connected-Vehicles (CV) crash warning systems on the drivers\u27 awareness of the imminent situation ahead to take timely crash avoidance action(s). Finally, a micro-simulation experiment was also conducted to evaluate the safety benefits of a proposed Variable Speed limit (VSL) strategy and CV technologies. The proposed VSL strategy and CV technologies were implemented and tested for a freeway section through the micro-simulation software VISSIM. The results of the above mentioned studies showed the impact of reduced visibility on traffic safety, and the effectiveness of different fog countermeasures
Initial study toward a methodical approach for the engineering of driver assistance technology
Bayerische Motoren Werke, Palo Alto, Calif.http://deepblue.lib.umich.edu/bitstream/2027.42/1283/2/92176.0001.001.pd
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