Demand Estimation and Bottleneck Management Using Heterogeneous Traffic Data

Congestion on urban and freeway networks has become a major problem, leading to increased travel times and reduced traffic safety. In order to suggest traffic management solutions to improve the transport system efficiency, it is important to capture the travel demand patterns, expressed as origin-destination (OD) matrices, and understand the mechanisms of traffic bottlenecks. The increasing availability of traffic data offers significant opportunities to effectively address these issues. The thesis uses heterogeneous traffic data to improve three important problems. The first problem relates to the dynamic OD estimation problem, which entails significant challenges due to its complexity. The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm has been commonly used to solve the problem, which can handle any available data that can improve the estimation accuracy. However, it encounters stability and convergence issues. The thesis proposes a general modification of SPSA, called cluster-wise SPSA (c-SPSA), that has more robust performance and finds better solutions. Its efficiency is demonstrated through simulation experiments for a network from Stockholm. The second problem focuses on the development of methods for utilizing heterogeneous traffic data for the analysis and management of freeway work zone and tunnel bottlenecks. Simulation is used as the means to evaluate and optimize various mitigation strategies for each case. The third problem analyzes multimodal impacts due to network disruptions for the case of tunnel bottlenecks, using a data-driven approach. Tunnel congestion is often dealt with temporary closures, which may cause significant disruptions. It is crucial to identify the potential multimodal impacts of such interventions so as to design efficient and proactive mitigation strategies. The thesis shows the benefits of combining multiple data sources to analyze the impacts of temporary tunnel closures for a freeway tunnel in Stockholm.QC 20180129</p

Demand Estimation and Bottleneck Management Using Heterogeneous Traffic Data

Congestion on urban and freeway networks has become a major problem, leading to increased travel times and reduced traffic safety. In order to suggest traffic management solutions to improve the transport system efficiency, it is important to capture the travel demand patterns, expressed as origin-destination (OD) matrices, and understand the mechanisms of traffic bottlenecks. The increasing availability of traffic data offers significant opportunities to effectively address these issues. The thesis uses heterogeneous traffic data to improve three important problems. The first problem relates to the dynamic OD estimation problem, which entails significant challenges due to its complexity. The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm has been commonly used to solve the problem, which can handle any available data that can improve the estimation accuracy. However, it encounters stability and convergence issues. The thesis proposes a general modification of SPSA, called cluster-wise SPSA (c-SPSA), that has more robust performance and finds better solutions. Its efficiency is demonstrated through simulation experiments for a network from Stockholm. The second problem focuses on the development of methods for utilizing heterogeneous traffic data for the analysis and management of freeway work zone and tunnel bottlenecks. Simulation is used as the means to evaluate and optimize various mitigation strategies for each case. The third problem analyzes multimodal impacts due to network disruptions for the case of tunnel bottlenecks, using a data-driven approach. Tunnel congestion is often dealt with temporary closures, which may cause significant disruptions. It is crucial to identify the potential multimodal impacts of such interventions so as to design efficient and proactive mitigation strategies. The thesis shows the benefits of combining multiple data sources to analyze the impacts of temporary tunnel closures for a freeway tunnel in Stockholm.QC 20180129</p

Improved traffic management for Swedish motorways using efficient ramp metering strategies: A case study for the Södra Länken tunnel

Motorway traffic management is becoming more and more crucial due to the increase of mobility in urban and motorway networks. The continuous development of intelligent traffic management strategies enables the deployment of advanced motorway control strategies without the need of infrastructure intervention. The project investigates and evaluates the potential of using adequate ramp metering strategies to mitigate traffic congestion on the Swedish motorways. In particular, the Södra Länken motorway tunnel in Stockholm is studied, which is prone to temporary closures due to congestion. The research involves, a data-driven analysis to understand the network traffic dynamics and to identify critical bottleneck locations and appropriate ramps where metering can be feasible (e.g. with sufficient storage capacity) and beneficial. Traffic simulation is used in order to assess the performance of the ramp metering strategies under different congestion patterns. The analysis evaluates and compares the feasibility as well as the impacts of the various ramp metering strategies in terms of their efficiency in reducing traffic congestion in the network. Recommendations and insights for the most promising metering strategies and their implementation requirements are provided to the traffic authorities as the basis for decisions on potential field deployments.Optimala påfartsstrategier för svenska motorväga

Real-Time Merging Traffic Control for Throughput Maximization at Motorway Work Zones

AbstractWork zones on motorways necessitate the drop of one or more lanes which may lead to significant reduction of traffic flow capacity and efficiency, traffic flow disruptions, congestion creation, and increased accident risk. Real-time traffic control by use of green-red traffic signals at the motorway mainstream is proposed in order to achieve safer merging of vehicles entering the work zone and, at the same time, maximize throughput and reduce travel delays. A particular issue addressed in this research is the investigation of the appropriate distance between the merge area and the traffic lights so as to lead, in combination with the employed real-time traffic control strategy, to the most efficient merging of vehicles. The control strategy applied for real-time signal operation is an ALINEA-like PI-type feedback regulator. In order to achieve maximum performance of the control strategy, some calibration of the regulator's parameters may be necessary. The calibration is first conducted manually, via a typical trial-and-error procedure. In an additional investigation, the recently proposed learning/adaptive algorithm AFT is employed in order to automatically fine-tune the regulator parameters. Experiments conducted with a microscopic simulator for a hypothetical work zone infrastructure, demonstrate the potential high benefits of the control scheme

The traffic impacts of tunnel closures : Evidence from sensors and Google Traffic Statistics

QC 20240104</p

Online Demand Forecasting with Spatial-Temporal Graph Attention Networks: A Proof of Concept

Predicting future demand from the current state of the network is an unsolved challenge in dynamic traffic management systems which show the current state of traffic as well as demand and supply forecasts for simulations of response plans. In the context of the TANGENT H2020 project, data-driven methodologies are developed focusing on the real-time demand prediction problem. This paper presents a spatial-temporal graph attention network (ST-GAT) to model: 1) the geometry of the network including centroids as demand generation points; 2) the temporal resolution of traffic counts. Preliminary experiments show promising outcomes when quasi-perfect synthetic data is used for training. Yet more research is needed to fully cater for the challenging requirements of the demand prediction task in real-time settings

Detailed list of sub-use cases, applicable forecasting methodologies and necessary output variables, Deliverable D4.4 of the H2020 project LEVITATE.

Work package 4 (WP4) within LEVITATE is concerned with gathering city visions and developing feasible paths of automated vehicles related interventions to achieve policy goals. City visions contributed to the project in assessing the impact indicators that are needed to be addressed for a useful policy support tool (PST). Previous deliverables of WP4 (deliverable 4.2 and 4.3) used backcasting methods to develop feasible pathways to reach these goals by using policy interventions related to connected and automated transport systems (CATS). These were carried out for the city of Vienna, Amsterdam andGreater Manchester.This deliverable summarises the work that has been conducted in the frame of WP4 and sets the scene for the core LEVITATE work packages (WPs 5, 6 and 7), which address the three main use cases of the project: Urban transport, Passenger cars and Freight transport. Further, the goal of this deliverable is to summarise a timewiseimplementation of different sub-use cases, and the forecasting methodologies that need to be employed to assess the direct, wider and systemic impacts of CATS. Discussion on the specific ways to study the impacts of the interventions using micro-simulationtechnique is conducted and the necessary outcome variables of the forecasting models are specified.The main contribution of deliverable 4.4 is a consolidated list of sub-use cases and output variables, and an indicative timewise implementation of the interventions. The list of subuse cases and interventions was evaluated against the available methods by performing a decision-making exercise among the project partners. From this evaluation, downselection was carried out during a plenary project meeting at the Hague in October 2019, to select the most appropriate and feasible sub-use cases and interventions. Later,these items were arranged on a timeline from present (2020) to 2040 to indicate possible arrival of the services, technologies or interventions due to the anticipated arrival of CATS. This gives an insight into what changes are to be expected in a future city.A small extract from Deliverable 3.2 (methods that could be applied to measure societal level impacts from CATS) is included in the current deliverable to provide a short summary of the methods available for forecasting societal level impacts. Since the systemic and wider impacts are somewhat dependent on the direct impact, traffic microsimulation method is the first choice to initially get direct impact. Therefore, this method is described in more detail. Further research is being undertaken in WPs 5, 6 and 7 to assess the impacts from specified sub-use cases in the most efficient way. To determinethese impacts quantitatively, a list of impact indicators is presented as output variables for the various methods that will be employed

Detailed list of sub-use cases, applicable forecasting methodologies and necessary output variables

Work package 4 (WP4) within LEVITATE is concerned with gathering city visions and developing feasible paths of automated vehicles related interventions to achieve policy goals. City visions contributed to the project in assessing the impact indicators that are needed to be addressed for a useful policy support tool (PST). Previous deliverables of WP4 (deliverable 4.2 and 4.3) used backcasting methods to develop feasible pathways to reach these goals by using policy interventions related to connected and automated transport systems (CATS). These were carried out for the city of Vienna, Amsterdam and Greater Manchester.This deliverable summarises the work that has been conducted in the frame of WP4 and sets the scene for the core LEVITATE work packages (WPs 5, 6 and 7), which address the three main use cases of the project: Urban transport, Passenger cars and Freight transport. Further, the goal of this deliverable is to summarise a timewise implementation of different sub-use cases, and the forecasting methodologies that need to be employed to assess the direct, wider and systemic impacts of CATS. Discussion on the specific ways to study the impacts of the interventions using micro-simulation technique is conducted and the necessary outcome variables of the forecasting models are specified.The main contribution of deliverable 4.4 is a consolidated list of sub-use cases and output variables, and an indicative timewise implementation of the interventions. The list of sub-use cases and interventions was evaluated against the available methods by performing a decision-making exercise among the project partners. From this evaluation, downselection was carried out during a plenary project meeting at the Hague in October 2019, to select the most appropriate and feasible sub-use cases and interventions. Later, these items were arranged on a timeline from present (2020) to 2040 to indicate possible arrival of the services, technologies or interventions due to the anticipated arrival of CATS. This gives an insight into what changes are to be expected in a future city.A small extract from Deliverable 3.2 (methods that could be applied to measure societal level impacts from CATS) is included in the current deliverable to provide a short summary of the methods available for forecasting societal level impacts. Since the systemic and wider impacts are somewhat dependent on the direct impact, traffic micro-simulation method is the first choice to initially get direct impact. Therefore, this method is described in more detail. Further research is being undertaken in WPs 5, 6 and 7 to assess the impacts from specified sub-use cases in the most efficient way. To determine these impacts quantitatively, a list of impact indicators is presented as output variables for the various methods that will be employed.</p

Examining road safety impacts of Green Light Optimal Speed Advisory (GLOSA) system

Green Light Optimal Speed Advisory (GLOSA) is a Day 1 C-ITS signage application, enabled by the C-ITS service “Signalised Intersections”. The application utilises traffic signal information and the current position of the vehicle to provide a speed recommendation in order for the drivers to pass the traffic lights during the green phase and therefore, reduce the number of stops, fuel consumption and emissions. The distance to stop, the plans for signal timing and the speed limit profile for the area are taken into account to calculate the speed recommendation displayed to the driver. GLOSA service is provided through ETSI G5 into the on-board computer of the vehicle or via mobile network into a smartphone application. In the era of CAVs, it would be useful for cities, various stakeholders, and transport planners to assess the societal impacts of such an application in an urban area and attempt to evaluate the benefits in relation to the relevant costs.</p