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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
221

Vom Lernen und Verlernen: Methodenhandbuch zur rassismuskritischen Aufarbeitung des NSU-Komplex

Zimmermann, Hannah, Klaus, Martina 12 April 2023 (has links)
Den NSU als Komplex (#NSU-Komplex) zu begreifen, bedeutet, dessen gesellschaftliche Entstehungsbedingungen zu verstehen und aus den Erkenntnissen Schlüsse für gelingende Präventionsarbeit zu ziehen. Das zentrale Ziel, das wir in diesem Methodenhandbuch für die Bildungsarbeit verfolgen, ist das Kennenlernen und die Sichtbarkeit der Betroffenenperspektiven nach dem Ansatz „Zuhören, Vertiefen, Aktiv werden“ von Ayşe Güleç und Fritz Laszlo Weber.1 In den Fokus gerückt werden die Lebensgeschichten und die Perspektiven der Angehörigen von Enver Şimşek, Abdurrahim Özüdoğru, Süleyman Taşköprü, Habil Kılıç, Mehmet Turgut, İsmail Yaşar, Theodoros Boulgarides, Mehmet Kubaşık, Halit Yozgat und Michèle Kiesewetter. Lehrkräfte und Multiplikator:innen sollen sich mit dem hier zur Verfügung gestellten Material ermutigt fühlen, mit jungen Menschen die Geschichten der Opfer des NSU zu erarbeiten, Migrationsgeschichte und -gegenwart in Deutschland kennenzulernen und sich mit Rassismus und seiner Wirkungsweise anhand des NSU-Komplexes auseinanderzusetzen.
222

Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network

Jaber, Sara, Mahdavi, Hassan, Bhouri, Neila 23 June 2023 (has links)
The paper proposes the management of bus disruption (e.g. fleet failure) and maintain a resilient transportation system through a synergy between shared autonomous vehicles and the existing public transport system based on the organizational structure and demand characteristics. The methodology is applied to the region of Rennes (France) and its surroundings.
223

Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory

Cantarella, Giuio Erberto, Cipriani, Ernesto, Gemma, Andrea, Giannattasio, Orlando, Mannini, Livia 23 June 2023 (has links)
This paper describes a general approach to the specification the stable regime speed-flow function, for motorways, as a part of the stable regime Stochastic Fundamental Diagram consistent with main assumptions of Transportation Systems Theory. Main original elements are: • Specification of speed-flow functions consistent with travel time function, such as BPR-like functions; • Calibration from disaggregate data, say data from single vehicle trajectories; • Specification of the speed r. v. distribution consistent with those used in RUT for route choice behavior modelling, such as Gamma, Inv-Gamma.
224

A RoundD-like Roundabout Scenario in CARLA Simulator

Nadar, Ali, Lafon, Mathis, Härri, Jérôme 23 June 2023 (has links)
Evaluating the challenges and opportunities of cooperative autonomous vehicles (CAV) require an adapted simulation methodology reproducing realistic driving and sensory contexts. In this paper, we propose a RounD-like CARLA scenario reproducing in CARLA the driving context recorded in the RounD dataset. We focus in particular on roundabout scenarios, as they are considered particularly challenging for CAV. We present the methodology followed to generate the CARLA scenario and describe challenges to reproduce trajectories corresponding to RounD. Origin and destination of vehicles, waypoint and speed are extracted from RounD for CARLA vehicles to closely reproduce the driving patterns observed in RounD. The benefit of such scenario are manyfold, such as evaluating control algorithms of CAVs, deep AI reinforcement learning, or vehicular sensor data sampling under realistic driving contexts. It notably will reduce the gap of AI mechanisms for CAV between simulation scenarios and realistic conditions.
225

A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions

Shakoori, Niloofar, De Nunzio, Giovanni, Leclercq, Ludovic 23 June 2023 (has links)
Given the increasing consciousness toward the environmental footprint of mobility, accommodating environmental objectives in existing transport planning strategies is imperative for research and practice. In this paper, we use the link macroscopic fundamental diagram (MFD) model to develop optimal routing strategies that minimize total system emissions (TSE) in multiple origin-destination (OD) networks. Piecewise linear (PWL) functions are used to approximate MFD for individual links, and to define link-level emissions. Dynamic network constraints, non-vehicle holding constraints, and convex formulations of the PWL functions are considered. Thus, the system-optimum dynamic traffic assignment (SO-DTA) problem with environmental objectives is formulated as a mixed integer linear program (MILP). Finally, on a synthetic network, numerical examples demonstrate the performance of the proposed framework.
226

Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics

Ayfantopoulou, Georgia, Mintsis, Evangelos, Maleas, Zisis, Mitsakis, Evangelos, Grau, Josep Maria Salanova, Mizaras, Vassilis, Tzenos, Panagiotis 23 June 2023 (has links)
Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki.
227

Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation

Genser, Alexander, Makridis, Michail A., Kouvelas, Anastasios 23 June 2023 (has links)
Emerging sensors and intelligent traffic technologies provide extensive data sets in a traffic network. However, realizing the full potential of such data sets for a unique representation of real-world states is challenging due to data accuracy, noise, and temporal-spatial resolution. Data assimilation is a known group of methodological approaches that exploit physics-informed traffic models and data observations to perform short-term predictions of the traffic state in freeway environments. At the same time, neural networks capture high non-linearities, similar to those presented in traffic networks. Despite numerous works applying different variants of Kalman filters, the possibility of traffic state estimation with deep-learning-based methodologies is only partially explored in the literature. We present a deep-learning modeling approach to perform traffic state estimation on large freeway networks. The proposed framework is trained on local observations from static and moving sensors and identifies differences between well-trusted data and model outputs. The detected patterns are then used throughout the network, even where there are no available observations to estimate fundamental traffic quantities. The preliminary results of the work highlight the potential of deep learning for traffic state estimation.
228

Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority

Keblawi, Mahmud, Toledo, Tomer 23 June 2023 (has links)
In traffic networks, appropriately determining the traffic signal plan of each intersection is a ünecessary condition for a reasonable level of service. This paper presents the development of a new system for automatically designing optimal actuated traffic signal plans with transit signal priority. It uses an optimization algorithm combined with a mesoscopic traffic simulation model to design and evaluate optimal traffic signal plans for each intersection in the traffic network, therefore reducing the need for human intervention in the design process. The proposed method can simultaneously determine the optimal logical structure, priority strategies, timing parameters, phase composition and sequence, and detector placements. The integrated system was tested by a real-world isolated intersection in Haifa city. The results demonstrated that this approach has the potential to efficiently design signal plans without human intervention, which can minimize time, cost, and design effort. It can also help uncover problems in the design that may otherwise not be detected.
229

Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control

Zoabi, Razi, Haddad, Jack 23 June 2023 (has links)
In this paper, a new cyclic structure of a max pressure travel time-based traffic signal control is developed to seek an optimal coordination in large-scale urban networks. The focus of the current paper is on dynamic manipulation of cycle lengths within cyclic structure. Following the application of a decentralized approach, which requires only local information in order to offer proper phase durations, the control strategy aims at maximizing the overall network throughput. Previous works of cyclic max-pressure control have presented a cyclic notion to actuate the controller in a cyclic manner. However, no input has been provided on the optimal cycle length for each intersection to be chosen in a network, and along with the dynamic and stochastic nature of the trips, it is not clear what are the main phases of the intersections and how to coordinate them. The developed cyclic max pressure control schemes are compared with an exiting cyclic scheme in the literature. Simulation results show that the newly proposed cyclic structure of the time-based approach offers better decision-making.
230

Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices

Moghaddam, S. M. Hassan Mahdavi, Ameli, Mostafa, Rao, K. Ramachandra, Tiwari, Geetam 23 June 2023 (has links)
This paper aims to develop and validate an efficient method for delineation of public transit analysis zones (PTTAZ), particularly for origin-destination (OD) matrix prediction for transit operation planning. Existing methods have a problem in reflecting the level of spatial precision, travel characteristics, travel demand growth, access to transit stations, and most importantly, the direction of transit routes. This study proposes a new methodology to redelineate existing traffic analysis zones (TAZ) to create PTTAZ in order to allocate travel demand to transit stops. We aim to achieve an accurate prediction of the OD matrix for public transportation (PT). The matrix should reflect the passenger accessibility in the socioeconomic and socio-spatial characterization of PTTAZ and minimize intrazonal trips. The proposed methodology transforms TAZ-based to PTTAZ-based data with sequential steps through multiple statistical methods. In short, the generation of PTTAZ establishes homogeneous sub-zones representing the relationship between passenger flow, network structure, land use, population, socio-economic characteristics, and, most importantly, existing bus transit infrastructure. To validate the proposed scheme, we implement the framework for India’s Vishakhapatnam bus network and compare the results with the household survey. The results show that the PTTAZ-based OD matrix represents a realistic scenario for PT demand.

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