"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:ec6e8914-2c6c-41d7-89b1-cbd39e7f6867","http://resolver.tudelft.nl/uuid:ec6e8914-2c6c-41d7-89b1-cbd39e7f6867","Understanding Traffic Events by Enriching Traffic Data with Geosocial Data","de Böck, Bas (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Software Technology)","Bozzon, Alessandro (mentor); Psyllidis, Achilleas (mentor); Houben, Geert-Jan (graduation committee); van Lint, Hans (graduation committee); Delft University of Technology (degree granting institution)","2018","Non-recurrent traffic events, consisting of events of an unpredictable nature such as incidents and vehicle breakdowns, can either directly or indirectly influence road traffic. A better understanding of these events could prove beneficial towards improving a multitude of facets concerning the management of the Dutch road network. Traditional traffic event detection, based on significant changes in traffic flow/speed characteristics, is often limited by sparse road sensor coverage. More importantly, traditional detection methods are unable to categorize and describe traffic events.
The aim of this study is to explore to which extent geosocial data (e.g., data from Twitter and Waze) could enrich traditional traffic data (e.g., traffic speed/flow data), in order to improve the detection, categorization, and description of traffic events in the Netherlands. In order to achieve this, a pipeline was designed for extracting knowledge on traffic events from geosocial data sources. We collected geosocial data from Twitter, Waze, and TomTom and used traffic data provided by DiTTLab. We specifically focused on reports by real road users, which we define as natural persons that report on their own account, therefore excluding all legal person entity accounts such as public/private organizations, and bots. A machine learning approach was applied to automatically classify tweets as either traffic event related or not. In order to categorize tweets into a traffic event category, a rule-based traffic domain annotator was created. Additionally, a geocoding method to link tweets to a geographic location was developed. As Waze and TomTom event reports are classified and geocoded by default, we could cluster these reports together with the processed tweets based on their categorical, spatial and temporal extent into a combined traffic event. These combined traffic event reports were then linked to traffic data, based on corresponding spatial and temporal aspects. In order to present the collected data, a web-based interactive map application was built.
This methodology was applied to data collected over the period from 05-12-2017 to 17-02-2018. From the set of collected tweets approximately 6.71% proved traffic event related. Based on a linear support vector machine classification model we achieved an average f1-score of 0.95 and an accuracy of 0.954, for detecting traffic event-related tweets. The rule-based traffic domain annotator showed an average f1-score of 0.874, and an accuracy of 0.964. The geocoding method proved able to geocode tweets to a location that covers all place indicators in a tweet in 86% of the evaluated cases. The remaining 14% of the tweets either got geocoded to a part of relevant indicators or to no relevant indicators at all. Our clustering approach is able to cluster 39.61% of the event reports into a traffic event report cluster consisting out of more than one event report, from which 48.66% could be linked to traffic data.
All in all, based on the achieved results, this work shows that geosocial data can be used to enrich traffic data towards the improvement of the detection, categorization, and description of non-recurrent traffic events.","Traffic Events; Traffic Congestion; Social Media","en","master thesis","","","","","","","","","","","","Computer Science | Software Technology","",""
"uuid:9020551a-9658-45bf-be80-e6ba736b55bc","http://resolver.tudelft.nl/uuid:9020551a-9658-45bf-be80-e6ba736b55bc","Modelling traffic in the Randstad using a dynamic zone model based on the Network Fundamental Diagram","Sloot, Mark (TU Delft Civil Engineering and Geosciences)","van Lint, Hans (graduation committee); Knoop, Victor (mentor); Yuan, Kai (mentor); Verbraeck, Alexander (graduation committee); Delft University of Technology (degree granting institution)","2019","The aim of this master thesis is to develop a dynamic zone-based traffic model for large areas, using the concept of the Network Fundamental Diagram (NFD), which relates the average internal flow (production) and speed in a zone to the number of vehicles in the same zone (accumulation). The model that has been developed is then applied to the Randstad area, and it is analyzed whether the model is able to reproduce the observed congestion patterns and travel times between the zones.","Network Fundamental Diagram","en","master thesis","","","","","","","","","","","","Civil Engineering | Transport and Planning","",""
"uuid:ecb3796f-ff68-400f-bf2d-a1ad3b340154","http://resolver.tudelft.nl/uuid:ecb3796f-ff68-400f-bf2d-a1ad3b340154","Analysis of current Dutch traffic management effectiveness with automated vehicles: a ramp-metering case study: Simulation Study","Zhou, Moyu (TU Delft Civil Engineering and Geosciences)","van Lint, Hans (graduation committee); Calvert, Simeon (mentor); Taale, Henk (mentor); Schakel, Wouter (mentor); Pan, Wei (graduation committee); Delft University of Technology (degree granting institution)","2019","Automated vehicles are conventional vehicles equipped with advanced sensors, controller and actuators. They achieve intelligent information exchange with the environment through the onboard sensing and cooperative system. vehicles are possible to have situation awareness and automatically analyze the safety and dangerous state of journeys. Finally vehicles can reach destinations following drivers' willing. The ongoing research on intelligent vehicles is mainly about improving the safety, comfort, efficiency and provide an excellent human-car interface. As a self-organizing system, the traffic system is quite complicated. There are many disturbance factors to lead to various traffic problems. One of the daily occurring problems is congestion on the motorway. In order to reduce congestion, Rijkswaterstaat applies various dynamic traffic management (DTM) measures to guide the traffic. It works well nowadays in conventional traffic. However, automated vehicles entered the market recently and will start to play an essential role in future traffic. The automated vehicles' reaction to DTM measures may be different from conventional vehicles while the traffic problems still exist. Therefore, it is necessary to research the effectiveness of current Dutch traffic management in automated vehicles. This thesis aims to investigate the effectiveness of current Dutch DTM measures with driver assistant and partially automated vehicles. Due to the time limitation, only the ramp metering measure will be researched through a simulation study. Therefore the main research question is 'How partial automated driving influences the performance of current Dutch dynamic traffic management system and how can this be evaluated via simulation?'. Three methods are applied, including literature review, simulation and statistical analysis. The literature part reviews levels of automation, various longitudinal and lateral vehicle motion models, which are chosen and modified in the simulation. Many ramp metering algorithms are also introduced in the literature review. The ramp metering controller in the simulation follows RWS algorithm. Besides, the motorway demand and the penetration rate of level 1 and 2 vehicles are two input of the simulation.
From the simulation results, it is concluded that the level 2 automation consisting of Adaptive Cruise Control (ACC) and Lane Change Assistance (LCA) system brings a negative impact on the motorway capacity. The ramp metering measure remains efficient if the penetration rate of level 2 vehicles is low. However, when the capacity reduces to the critical flow set up in the ramp metering controller, Ramp metering loses its efficiency. The parameters in the ramp metering controller therefore, require an update. For further research, it is recommended to simulate the same scenarios with different ramp metering algorithms. Since the functions of the algorithms are different, there might be other robust control algorithms for automated vehicles. Besides, another limitation of this thesis is that the automation system in level 2 vehicles is defined as Adaptive Cruise Control (ACC) plus Lane Change Assistance (LCA) system. Other partial automation systems may have a different effect on the performance of ramp metering. This thesis can be expanded by research the ramp metering performance under various types of partial automation systems.","Dynamic Traffic Management; Ramp Metering","en","master thesis","","","","","","","","","","","","Civil Engineering | Transport and Planning","",""