Lukas Ambühl

Last Name

Ambühl

First Name

Lukas

ORCID

0000-0001-8835-0950

Organisational unit

01109 - Lehre Bau, Umwelt und Geomatik

Show all metadata

Search Results

Publications1 - 10 of 17

Understanding traffic capacity of urban networks
Loder, Allister; Ambühl, Lukas; Menendez, Monica; et al. (2019)
Scientific Reports
Traffic in an urban network becomes congested once there is a critical number of vehicles in the network. To improve traffic operations, develop new congestion mitigation strategies, and reduce negative traffic externalities, understanding the basic laws governing the network’s critical number of vehicles and the network’s traffic capacity is necessary. However, until now, a holistic understanding of this critical point and an empirical quantification of its driving factors has been missing. Here we show with billions of vehicle observations from more than 40 cities, how road and bus network topology explains around 90% of the empirically observed critical point variation, making it therefore predictable. Importantly, we find a sublinear relationship between network size and critical accumulation emphasizing decreasing marginal returns of infrastructure investment. As transportation networks are the lifeline of our cities, our findings have profound implications on how to build and operate our cities more efficiently.
On the application of variational theory to urban networks
Tilg, Gabriel; Ambühl, Lukas; Batista, Sergio; et al. (2021)
Transportation Research Part B: Methodological
The well-known Lighthill–Whitham–Richards (LWR) theory is the fundamental pillar for most macroscopic traffic models. In the past, many methods were developed to numerically derive solutions for LWR problems. Examples for such numerical solution schemes are the cell transmission model, the link transmission model, and the variational theory (VT) of traffic flow. So far, the eulerian formulation of VT found applications in the fields of traffic modelling, macroscopic fundamental diagram estimation, multi-modal traffic analyses, and data fusion. However, these studies apply VT only at the link or corridor level. To the best of our knowledge, there is no methodology yet to apply VT at the network level. We address this gap by developing a VT-based framework applicable to networks. Our model allows us to account for source terms (e.g. inflows and outflows at intersections) and the propagation of spillbacks between adjacent corridors consistent with kinematic wave theory (KWT). We show that the trajectories extracted from a microscopic simulation fit the predicted traffic states from our model for a simple intersection with both source terms and spillbacks. We also use this simple example to illustrate the accuracy of the proposed model, and the ability to model complex bottlenecks. Additionally, we apply our model to the Sioux Falls network and again compare the results to those from a microscopic KWT simulation. Our results indicate a close fit of traffic states, but with substantially lower computational cost. The developed methodology is useful for extending existing VT applications to the network level, for network-wide traffic state estimations in real-time, or other applications within a model-based optimization framework.
Fitting empirical fundamental diagrams of road traffic
Bramich, Daniel M.; Menendez, Monica; Ambühl, Lukas (2022)
IEEE Transactions on Intelligent Transportation Systems
Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10,150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question ``Which model for the functional form of an empirical fundamental diagram is currently the best?''. The word ``currently'' in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms.
Disentangling the city traffic rhythms
Ambühl, Lukas; Loder, Allister; Leclercq, Ludovic; et al. (2021)
Transportation Research Part C: Emerging Technologies
Urban road transportation performance is the result of a complex interplay between the network supply and the travel demand. Fortunately, the framework around the macroscopic fundamental diagram (MFD) provides an efficient description of network-wide traffic performance. In this paper, we show how temporal patterns of vehicle traffic define the performance of urban road networks. We present two high-resolution traffic datasets covering a year each. We introduce a methodology to quantify the similarity of macroscopic traffic patterns. We do so by using the concepts of the MFD and a dynamic time warping (DTW) based algorithm for time series. This allows us to derive a few representative MFD clusters that capture the essential macroscopic traffic patterns. We then provide an in-depth analysis of traffic heterogeneity in the network which is indicative of the previously found clusters. Thereupon, we define a parsimonious classification approach to predict the expected MFD clusters early in the morning with high accuracy.
From Corridor to Network Macroscopic Fundamental Diagrams: A Semi-Analytical Approximation Approach
Tilg, Gabriel; Ambühl, Lukas; Batista, Sérgio F.A.; et al. (2023)
Transportation Science
The design of network-wide traffic management schemes or transport policies for urban areas requires computationally efficient traffic models. The macroscopic fundamental diagram (MFD) is a promising tool for such applications. Unfortunately, empirical MFDs are not always available, and semi-analytical estimation methods require a reduction of the network to a corridor that introduces substantial inaccuracies. We propose a semi-analytical methodology to estimate the MFD for realistic urban networks without the information loss induced by the reduction of networks to corridors. The methodology is based on the method of cuts but applies to networks with irregular topologies, accounts for different spatial demand patterns, and determines the upper bound of network flow. Therefore, we consider both flow conservation and the effects of spillbacks at the network level. Our framework decomposes a given network into a set of corridors, creates a hypernetwork, including the impacts of source terms, and then treats the dependencies across corridors (e.g., because of turning flows and spillbacks). Based on this hypernetwork, we derive the free-flow and capacity branch of the MFD. The congested branch is estimated by considering gridlock characteristics and utilizing recent advancements in MFD research. We showcase the applicability of the proposed methodology in a case study with a realistic setting based on the Sioux Falls network. We then compare the results to the original method of cuts and a ground truth derived from the cell transmission model. This comparison reveals that our method is more than five times more accurate than the state of the art in estimating the network-wide capacity and jam density. Moreover, the results clearly indicate the MFD’s dependency on spatial demand patterns. Compared with simulation-based MFD estimation approaches, the potential of the proposed framework lies in the modeling flexibility, explanatory value, and reduced computational cost.
Time-to-Green predictions
Genser, Alexander; Ambühl, Lukas; Yang, Kaidi; et al. (2020)
Time-to-Green Predictions for Fully-Actuated Signal Control Systems With Supervised Learning
Genser, Alexander; Makridis, Michail; Yang, Kaidi; et al. (2024)
IEEE Transactions on Intelligent Transportation Systems
Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases’ duration. The performance of a Linear Regression (LR), Random Forest (RF), a light gradient-boosting machine (LightGBM), a bidirectional Long-Short-Term-Memory neural network (BiLSTM) and a Temporal Convolutional Network (TCOV) are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that state of the art machine learning models outperform conventional prediction methods.
On the modeling of passenger mobility for stochastic bi-modal urban corridors
Dakic, Igor; Ambühl, Lukas; Schümperlin, Oliver; et al. (2019)
Transportation Research Part C: Emerging Technologies
Wie viel Verkehr für eine Stadt? Ein makroskopischer Ansatz
Loder, Allister; Ambühl, Lukas; Axhausen, Kay W. (2020)
Strassenverkehrstechnik
Der Ansatz des makroskopischen Fundamentaldiagramms (MFD) erlaubt die Bestimmung der Kapazität eines gesamten städtischen Strassennetzes und nicht nur die Kapazität einzelner Netzelemente. In diesem Beitrag wird neben einer Einführung in das MFD im ersten Teil darauf eingegangen, wie die Kapazität eines Netzes quantitativ bestimmt werden kann und welchen Einfluss die Netzstruktur auf die Kapazität hat. Im zweiten Teil wird zuerst beschrieben, wie das MFD zum multimodalen MFD erweitert wird, welches die Wechselwirkungen zwischen verschiedenen Fahrzeugtypen, z. B. Pkw und Bus, beschreibt. Auf Basis des multimodalen MFDs werden dann Ansätze zusammengefasst, mit denen die Aufteilung der Netzressourcen zwischen Verkehrsmitteln für den grösstmöglichen Durchsatz an Fahrzeugen oder Passagieren analysiert werden kann.
Time-to-Green predictions for fully-actuated signal control systems with supervised learning
Genser, Alexander; Makridis, Michail; Yang, Kaidi; et al. (2022)
arXiv
Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications.

Publications1 - 10 of 17

Lukas Ambühl

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results