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.

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.

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.

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.

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.

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.

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.

Menschen wie Sie bereichern unsere Gesellschaft mit Wissen, Erfahrungen und vielfältigem Engagement. Wir können also von Glück reden, dass Sie, genauso wie gut 27 Prozent der über 60-jährigen Dresdnerinnen und Dresdner in unserer Stadt leben. Wie die Jugend, so hat auch das Alter viele Gesichter. Und so verschieden die Gesichter, so vielfältig sind auch Ihre Bedürfnisse als Seniorin oder Senior – von aktiv bis unterstützungsbedürftig. Dass wir die unterschiedlichen Bedürfnisse ernst nehmen, davon zeugt die 8. Auflage dieser Broschüre. Differenzierte Angebote für individuelle Wünsche und Erfordernisse sind in diesem Wegweiser zusammengestellt. Redaktionsschluss: Dezember 2019

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Jubiläumsbroschüre: 75 Jahre Solidarität