Alexander Genser

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Genser

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Alexander

ORCID

0000-0002-7454-0443

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Publications 1 - 10 of 31

OptFlow
Genser, Alexander; Makridis, Michail; Kouvelas, Anastasios (2022)
Im Zuge des Projektes «Opt-Flow» in Zusammenarbeit mit der Dienstabteilung Verkehr (DAV) der Stadt Zürich, wurde eine Beurteilung der Genauigkeit von Verkehrsfluss- und Reisezeitmessung durch verschiedene Sensorik vorgenommen. Von besonderer Interesse sind dabei neu installierte Thermalkameras mit eingebauter WiFi-Schnittstelle. Es werden Verkehrsflüsse und Reisezeiten in einem Testperimeter (in Folge auch Untersuchungsgebiet genannt) in Oerlikon, Zürich erfasst und mit diversen neuartigen Datenquellen verglichen. Final wird die Qualität der ermittelten Verkehrsgrössen in zwei Fallstudien mit Resultaten von manuell ausgewerteten Messkampagnen beurteilt. Zudem wird eine Fallstudie, basierend auf einer deskriptiven Datenanalyse, durchgeführt.
A novel, modular validation framework for collision avoidance of automated vehicles at road junctions
Nitsche, Philippe; Welsh, Ruth H.; Genser, Alexander; et al. (2018)
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
This paper presents a new validation method for automated driving systems at road junctions. The method comprises the clustering of critical traffic scenarios at junctions as well as a simulation and evaluation framework to validate those scenarios. The safety performance indicators selected and implemented in the framework can be seen as a new reference for conducting virtual tests at junctions. The applicability of the framework is demonstrated by an experiment based on a selected car-to-car collision scenario. Considering the current progression of automated transport, this work is highly relevant for virtual testing procedures and is an important step towards approval and certification of automated vehicles.
Exploring antifragility in traffic networks
Sun, Linghang; Makridis, Michail; Genser, Alexander; et al. (2024)
2024 TRB Annual Meeting Online Program Archive
The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that often struggle to cope with real-world uncertainties, leading to a significant decline in their effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to enhancing the robustness or resilience of transportation systems against disruptions, in this paper, we use the concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such disruptions. Hence, modern transport systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) algorithm to regulate perimeter control in a two-region urban traffic network to exploit and strengthen the learning capability of RL under disruptions and achieve antifragility. By incorporating antifragility terms based on the change rate and curvature of the traffic state into the RL framework, the proposed algorithm further gains knowledge of the traffic state, which helps in anticipating imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to enhance the performance under disruption scenarios. When compared to a state-of-the-art model predictive control approach and a state-of-the-art RL algorithm, our proposed method demonstrates two antifragility-related properties: (a) gradual performance improvement under disruptions of similar magnitude; and (b) increasingly superior performance under growing disruptions.
Identification of critical ride comfort sections by use of a validated vehicle model and Monte Carlo simulations
Genser, Alexander; Nitsche, Philippe; Kouvelas, Anastasios (2019)
2019 IEEE Intelligent Transportation Systems Conference (ITSC)
A coherent way to enhance the user acceptance of autonomous vehicles (AV) is to ensure maximum ride comfort along the driven route. This paper proposes a sub-microscopic simulation framework that can be utilized to assess the ride comfort based on data from vehicle dynamics. In a future connected vehicle environment, this work can be used to enable an optimized route and motion planning, by avoiding sections with poor ride comfort and/or adapting the driving style and behavior. The developed methodology proposes a process chain for producing accurate and representative comfort estimates, by utilizing a road surface model, a non-linear model optimization, and Monte Carlo simulations. A case study with three real road sites demonstrates the effective tuning of the framework with real data and achieves high-resolution comfort results. The simulation investigations of the developed framework provide results and insights that justify the importance of enhancing available data sources with ride comfort data.
Critical ride comfort detection for automated vehicles
Genser, Alexander; Spielhofer, Roland; Nitsche, Philippe; et al. (2021)
SVT Working Papers
In a future connected vehicle environment, an optimized route and motion planning should not only fulfill efficiency and safety constraints but also minimize vehicle motions and oscillations, causing poor ride comfort perceived by passengers. This work provides a framework for a large-scale and cost-efficient evaluation to address AV’s ride comfort and allow the comparison of different comfort assessment strategies. The proposed tool also gives insights to comfort data, allowing for the development of novel algorithms, guidelines, or motion planning systems incorporating passenger comfort. A vehicle-road simulation framework utilizable to assess the most common ride comfort determination strategies based on vehicle dynamics data is presented. The developed methodology encompasses a road surface model, a non-linear vehicle model optimization, and Monte Carlo simulations to allow for an accurate and cost-efficient generation of virtual chassis acceleration data. Ride comfort is determined by applying a commonly used threshold method and an analysis based on ISO 2631. The two methods are compared against comfort classifications based on empirical measurements of the International Roughness Index (IRI). A case study with three road sites in Austria demonstrates the framework’s practical application with real data and achieves high-resolution ride comfort classifications. The results highlight that ISO 2631 comfort estimates are most similar to IRI classifications and that the thresholding procedure detects preventable situations but also over- or underestimates ride comfort. Hence, the work shows the potential risk of negative ride comfort of AVs using simple threshold values and stresses the importance of a robust comfort evaluation method for enhancing AVs’ path and motion planning with maximal ride comfort.
Dynamic congestion pricing for multi-region networks: A traffic equilibria approach
Genser, Alexander; Kouvelas, Anastasios (2019)
The growing number of people living in cities results in rising mobility demand, and as aconsequence, the limited capacity of traffic networks gets more stressed. Hence, congested network links are causing travel delays and negative impacts on the environment, postulating for a methodology to overcome this challenge. Considering the broad range of traffic management systems, congestion pricing is a very effective tool to tackle today’s cities traffic problems. Different strategies are available in literature or even applied in real-world that show a positive effect on the traffic situation. This paper proposes a framework design that allows the testing of pricing policies and to evaluate their performance in alleviating congestion. The study implements a multi-regionurban network, where the urban regions are considered as homogeneous and replicated with a representative Macroscopic Fundamental Diagram (MFD). To assess the impact that different pricing policies may have on traffic behavior, a route choice algorithm is utilized and a concept for the computation of the dynamic user equilibrium, as well as the system optimum, are proposed. A case study is presented, where the modeling approach is applied to the heterogeneous road traffic network of the city of Zurich, Switzerland.
Real-time traffic state estimation with the application of deep learning techniques
Genser, Alexander (2022)
Optimum route guidance in multi-region networks
Genser, Alexander; Kouvelas, Anastasios (2020)
An experimental urban case study with various data sources and a model for traffic estimation
Genser, Alexander; Hautle, Noel; Makridis, Michail; et al. (2021)
arXiv
Accurate estimation of the traffic state over a network is essential since it is the starting point for designing and implementing any traffic management strategy. Hence, traffic operators and users of a transportation network can make reliable decisions such as influence/change route or mode choice. However, the problem of traffic state estimation from various sensors within an urban environment is very complex for several different reasons, such as availability of sensors, different noise levels, different output quantities, sensor accuracy, heterogeneous data fusion, and many more. To provide a better understanding of this problem, we organized an experimental campaign with video measurement in an area within the urban network of Zurich, in Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras by the city’s authorities, processed video data and the Google Distance Matrix. We assess the different data sources, and we propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources. Comparative results with ground-truth data (derived from video measurements) show the efficiency and robustness of the proposed methodology.
Optimum route guidance in multi-region networks
Genser, Alexander; Kouvelas, Anastasios (2020)
Dynamic congestion pricing is a useful tool not only to mitigate traffic congestion but also to influence people’s route choice. Dynamic tolls at the entrance of a protected region can give network users an incentive to reconsider their travel route and allow traffic management operators to direct a transportation network towards the system optimum. Computation of the optimal route guidance can be utilized as a benchmark for the pricing strategy of a corridor and significantly improve the performance of a transportation network. In recent work, decentralized control approaches have been applied, implementing a multi-region-network, where the urban regions are considered as homogeneous, and replicated with a representative Macroscopic Fundamental Diagram (MFD). The studies have tried to tackle the optimal route guidance problem by applying a Nonlinear Model Predictive Control (NMPC). Considering the shortcomings of local operating controllers and the well-known limitations of nonlinear optimization, we design a linear formulation of the optimization problem at the network level. This work evaluates the performance of a Linear Rolling Horizon Optimization (LRHO) which is applied to a multi-region-network. Results show a significant improvement in network performance by applying the determined dynamic routing compared to standard computation procedures of route choices. This work demonstrates that a linear formulation can be applied to the optimal route guidance problem, reduces the computational burden of centralized control approaches, and can potentially be utilized in real-time applications. Furthermore, the procedure can perform as a benchmark for traffic control with congestion pricing in future research.

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Alexander Genser

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