A Microscopic Simulation Study of Applications of Signal Phasing and Timing Information in a Connected Vehicle Environment

The connected vehicle technology presents an innovative way of sharing information between vehicles and the transportation infrastructure through wireless communications. The technology can potentially solve safety, mobility, and environmental challenges that face the transportation sector. Signal phasing and timing information is one category of information that can be broadcasted through connected vehicle technology. This thesis presents an in-depth study of possible ways signal phasing and timing information can be beneficial as far as safety and mobility are concerned. In total, three studies describing this research are outlined. The first study presented herein focuses on data collection and calibration efforts of the simulation model that was used for the next two studies. The study demonstrated a genetic algorithm procedure for calibrating VISSIM discharge headways based on queue discharge headways measured in the field. Video data was used to first compute intersection discharge headways for individual vehicle queue position and then to develop statistical distributions of discharge headways for each vehicle position. Except for the 4th vehicle position, which was best fitted by the generalized extreme value (GEV) distribution, the Log-logistic distribution was observed to be the best fit distribution for the rest of vehicle positions. Starting with the default values, the VISSIM parameters responsible for determining discharge headways were heuristically adjusted to produce optimal values. The optimal solutions were achieved by minimizing the Root Mean Square Error (RMSE) between the simulated and observed data. Through calibration, for each vehicle position, it was possible to obtain the simulated headways that reflect the means of the observed field headways. However, calibration was unable to replicate the dispersion of the headways observed in the field mainly due to VISSIM limitations. Based on the findings of this study, future work on calibration in VISSIM that would account for the dispersion of mixed traffic flow characteristics is warranted. The second study addresses the potential of connected vehicles in improving safety at the vicinity of signalized intersections. Although traffic signals are installed to reduce the overall number of collisions at intersections, rear-end collisions are increased due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposed and evaluated an Advanced Stop Assist System (ASAS) at signalized intersections by using Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication. The proposed system utilizes communication data, received from Roadside Unit (RSU), to provide drivers in approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking upon a yellow and red signal indication. A simulation test bed was modeled using VISSIM to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that at least 60% penetration rate is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections. The last study addresses the fact that achieving smooth urban traffic flow requires reduction of excessive stop-and-go driving on urban arterials. Smooth traffic flow comes with several benefits including reduction of fuel consumption and emissions. Recently, more research efforts have been directed towards reduction of vehicle emissions. One such effort is the use of Green Light Optimal Speed Advisory (GLOSA) systems which use wireless communications to provide individual drivers with information on the approaching traffic signal phase and advisory speeds to arrive at the intersection on a green phase. Previously developed GLOSA algorithms do not address the impact of time to discharge queues formed at the intersection. Thus, this study investigated the influence of formed intersection queues on the performance of GLOSA systems. A simulation test-bed was modeled inside VISSIM to evaluate the algorithm’s effectiveness. Three simulation scenarios were designed; the baseline with no GLOSA in place, scenario 2 with GLOSA activated and queue discharge time not considered, and scenario 3 with GLOSA activated and where queue dissipation time was used to compute advisory speeds. At confidence level the results show a significant reduction in the time spent in queue when GLOSA is activated (scenarios 2 and 3). The change in the average number of stops along the corridor was found not to be significant when the base scenario was compared against scenario 2. However, a comparison between scenarios 2 and 3 demonstrates a significant reduction in the average number of stops along the corridor, and also in the time spent waiting in queue

MAVEN Deliverable 7.2: Impact Assessment - Technical Report

This deliverable focuses on an important topic within the MAVEN project - evaluation of the project impact. This is an important step that will allow us to say what the results and impact of the different technologies, functionalities as well as assumptions are. It covers different dimensions of the impact assessment as stated in the Deliverable D7.1 - Impact assessment plan [10]. The field tests proved that the technology in the vehicle works together with the infrastructure and the solution is technically feasible. This was demonstrated also during particular events and is reported in the attached test protocols. At the same time, the emulation and simulation in Dominion software proved the functionality, for example with respect to the cooperative perception or safety indicators. The tests also proved that the key performance indicator "minimum time to the collision" decreases when applying the cooperative sensing. Also, the number of human interventions needed was zero in all the tests. This deliverable also discussed selected results of a detailed user survey aiming at understanding the expected impacts and transition of automated vehicles. The overall number of respondents reached 209. The responses have revealed some interesting facts. For example, over 80% of the respondents believe that CAVs will decrease the number of traffic accidents. Similarly, about 70% of the respondents expect improvements in traffic congestions. Over 82% of respondents declared that they would accept some detour when driving if it helps the overall traffic situation. The literature review, however, indicated that autonomous vehicles will have either a positive or a negative effect on the environment, depending on the policies. For example, opening cars as a mode of transport to new user groups (seniors, children etc.) together with improvements of the traffic, flow parameters can increase the traffic volume on roads. Policy makers shall focus on the integration of the CAVs into a broader policy concept including car or ride-sharing, electromobility and others. In order to evaluate the transition, for example, the influence of different penetration rates of CAVs on the performance, a microscopic traffic simulation was performed. Here the particular MAVEN use cases, as well as their combination, was addressed. The results of the simulation are rather promising. The potential for improvements in traffic performance is clearly there. It was demonstrated that a proper integration of CAVs into city traffic management can, for example, help with respect to the environmental goals (Climate Action of the European Commission) and reduce CO2 emissions by up to 12 % (a combination of GLOSA and signal optimization). On corridors with a green wave, a capacity increase of up to 34% was achieved. The conclusions from this project can be used not only by other researchers but mainly by traffic managers and decision-makers in cities. The findings can get a better idea about the real impacts of particular use cases (such as green wave, GLOSA and others) in the cities. An important added value is also the focus on the transition phase. It was demonstrated that already for lower penetration rates (even 20% penetration of automated vehicles), there are significant improvements in traffic performance. For example, the platooning leads to a decrease of CO2 emissions of 2,6% or the impact indicator by 17,7%

Analysis, simulation and testing of ITS applications based on wireless communication technologies

Intelligent Transportation Systems (ITS) aim to improve road transport safety and efficiency, to manage road networks in the interest of the society and to provide real time responses to events. In order to reach these goals, real time feedback to the drivers is expected through the integration of telecommunications, sensing and information technologies with transport engineering. Wireless communication technologies, that have been used in industrial applications for more than 30 years, play a crucial role in ITS, as based on the concept of multiple devices (on both vehicle and infrastructure side) interconnected in different ways. Connectivity, in tandem with sensing technologies, is fuelling the innovations that will inevitably lead to the next big opportunity for road transport: autonomous vehicles. Therefore, this study has investigated - through analysis, simulation and field testing – on applications based on wireless communication technologies meant to support both Data acquisition and Data diffusion as fundamental aspects/ phases in ITS, where data is widely individuated as being the key element

An Emulation Framework for Evaluating V2X Communications in C-ITS Applications

C-ITS enhances transportation systems with advanced communication tech, enabling vehicle-to-vehicle and vehicle-to-infrastructure data exchange for real-time decision-making. The thesis explores C-ITS concepts, DSRC, and C-V2X tech, and proposes a versatile C-ITS framework for app prototyping and communication evaluation. Real-world tests and simulations validate its potential to improve road safety and efficiency, suggesting integration opportunities for stakeholders and promoting a smarter, sustainable transportation ecosystem

2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

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

Congestion adaptive traffic light control and notification architecture using Google maps APIs

Mishra, S., Bhattacharya, D., & Gupta, A. (2018). Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs. Data, 3(4), [67]. DOI: 10.3390/data3040067Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.publishersversionpublishe

MULTILINE HOLDING CONTROL AND INTEGRATION OF COOPERATIVE ITS

Transportation is an important sector of the global economy. The rapid urbanization and urban sprawl comes with continuous demand for additional transportation infrastructure in order to satisfy the increasing and variable demand. Public transportation is a major contributor in alleviating traffic congestion in the modern megacities and provide a sustainable alternative to car for accessibility. Public transport operation is inherently stochastic due to the high variability in travel times and passenger demand. This yields to disruptions and undesired phenomena such as vehicles arriving in platoons at stops. Due to the correlation between the headway between vehicles and passenger demand, bunching leads to long waiting time at stops, overcrowded vehicles, discomfort for the passengers and from the operators side poor management of available resources and overall a low of service of the system. The introduction of intelligent transport systems provided innovative applications in order to monitor the operation, collect data and react dynamically to any disruption of the transit system. Advanced Public Transport Systems extended the range of control strategies and their objectives beyond schedule adherence and reliance on historical data alone. Among strategies, holding is a thoroughly investigated and applicable control strategy. With holding, a vehicle is instructed to remain at a designated stop for an additional amount of time after the completion of dwell time, until a criterion is fulfilled. Depending on the characteristics of the line the criterion aim for schedule adherence or regularity or minimization of passenger costs and its components. So far, holding is used for regulating single line operation. Beyond single line, it has been used for transfer synchronization at transfer hubs and recently has been extended to regulate the operation on consecutive stops that are served by multiple lines. The first part of this dissertation is dedicated to real time holding control of multiple lines. A rule based holding criterion is formulated based on the passenger travel time that accounts for the passengers experiencing the control action. Total holding time is estimated based on the size of all passenger groups that interact. The formulated criterion can be applied on all different parts of trunk and branch network. Additionally, the criterion is coupled with a rule based criterion for synchronization and the decision between the two is taken based on the passenger cost. The criterion has been tested for different trunk and branch networks and compared with different control schemes and its performance has been assessed using regularity indices as well as passenger cost indicators for the network in total but also per passenger group. Finally, an analysis has been conducted in order to define under which network and demand configuration multiline control can be preferred over single line control. Results shown that under specific demand distributions multiline control can outperform single line control in network level. Continuously new technologies are introduced to transit operation. Recently, Cooperative Intelligent Transport Systems utilized in the form of Driver Advisory Systems (DAS) shown that can provide the same level of priority with transit signal priority without changing the time and the phases of a traffic light. However, until now the available DASs focus exclusively on public transport priority neglecting completely the sequence of the vehicles and the effects on the operation. In the second part of the dissertation, two widely used DASs are combined with holding in order to meet both the objective of reducing the number of stops at traffic signals and at the same time maintain regularity. Two hybrid controllers are introduced, a combination of two holding criteria and a combination of holding and speed advisory. Both controllers are tested using simulation in comparison to the independent application of the controllers and different levels of transit signal priority. The hybrid controllers can drastically reduce transit signal priority requests while they manage to achieve both objectives