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Workshop “Road Traffic Flow: Analysis, Optimization and Control”
Oct 21, 2021 - Oct 22, 2021
Road Traffic Flow – Analysis, Optimization and Control
Transportation plays a pivotal role in a society’s economic and social welfare. Overly congested road traffic networks account for several billions of euros in cost, in the Netherlands alone. Since congestion itself can have many different causes (e.g., too much traffic, accidents, and bad network design), it is an inherently interesting and important topic for multiple scientific research areas, ranging from social sciences to civil engineering, mathematics and physics. Each area focuses on their favorite topics and yields interesting and important insights that lead to effective measures to control traffic flows and optimize transportation network performance.
To encourage interaction and collaboration between road-traffic researchers, we are hosting an engaging two-day workshop entitled “Road Traffic Flow: Analysis, Optimization, and Control” at the workshop institute Eurandom at Eindhoven University of Technology on October 21st and 22nd. The main goal of this workshop is to stimulate the cross-disciplinary collaboration between scientific areas that focus on similar problems, like civil engineering and operations research, which can lead to a synergy that is beneficial for all road traffic research.
The presentations during the workshop will mostly be given by researchers from the Netherlands, ranging from PhD students to full professors, supplemented with keynote and tutorial presentations from internationally renowned scientists.
The workshop will be held in a hybrid format, meaning that the workshop can both be followed online and on-campus. As organizers, we encourage on-campus attendance, since in this way we can meet each other in person. For attendees that are unable to attend the workshop on campus, every session will be streamed live via Zoom.
|Marko Boon||TU Eindhoven|
|Sindo Núñez Quijea||University of Amsterdam|
|Jan-Kees van Ommeren||University of Twente|
|Jaap Storm||TU Eindhoven|
|Rik Timmerman||TU Eindhoven|
|Carolina Osorio||HEC Montreal|
|Eddie Wilson||University of Bristol|
|Peter Wagner||DLR (Deutsches Zentrum für Luft- und Raumfahrt)|
Invited speakers (confirmed)
Follow this link to the schedule
Modelling humans as humans in mixed traffic with Connected Automated Vehicles: a tale of human cognition
With the emergence of Connected Automated Vehicles (CAV), the need to make human drivers more human increases to allow us to replicate and forecast the resulting effects of their interactions in mixed traffic. Interactions between CAVs and their drivers, and interaction with other human drivers involve new types of complex behavioural processes. The inclusion of human factors in mathematical models is proving crucial to allow complex driving behaviour and interactions to be explicitly considered to capture driving phenomena. In this talk, we will dive into what lies at the heart of human driving and how we can model some of these behaviours to include human cognition in simulation in a way that still remains computationally achievable.
Contemporary Techniques for Urban Transport Operations Management
I will discuss two techniques that solve real-time transport operations in urban network settings. The first part of my talk will present a modeling technique used to translate any (ridge) regression problem into a matrix completion problem that can be solved efficiently using block-coordinate descent techniques. We apply this to forecasting high-resolution traffic states from point sensors in signalized networks. The approach is particularly suitable for large datasets. I will discuss some of the modeling advantages and guarantees of performance in the form of generalization errors. The second part of my talk will focus on lightweight algorithmic approaches for managing two types of systems: (1) urban network traffic signal control and (2) carsharing operations. I will show how to attack both types of problems using Lyapunov optimization techniques, which ensure stability at the network level while employing simple localized solution approaches.
Road traffic estimation and distribution-based route selection
In route selection problems, the driver’s personal preferences will determine whether she prefers a route with a travel time that has a relatively low mean and high variance over one that has relatively high mean and low variance. In practice, however, such risk aversion issues are often ignored, in that a route is selected based on a single-criterion Dijkstra-type algorithm. In addition, the routing decision typically does not take into account the uncertainty in the estimates of the travel time’s mean and variance. This paper aims at resolving both issues by setting up a framework for travel time estimation.
In our framework, the underlying road network is represented as a graph. Each edge is subdivided into multiple smaller pieces, so as to naturally model the statistical similarity between road pieces that are spatially nearby. Relying on a Bayesian approach, we construct an estimator for the joint per-edge travel time distribution, thus also providing us with an uncertainty quantification of our estimates. Our machinery relies on establishing limit theorems, making the resulting estimation procedure robust in the sense that it effectively does not assume any distributional properties. We present an extensive set of numerical experiments that demonstrate the validity of the estimation procedure and the use of the distributional estimates in the context of data-driven route selection.
Multi-lane traffic: vehicle-level ramp metering and lane change control
In motorway traffic, the road capacity reduces if congestion sets in. This phenomenon is called the capacity drop. It is hence advisable to keep the motorway free of congestion. To avoid congestion on the motorway, inflow to the motorway can be limited by means of ramp metering. This means that traffic on-ramps is hold back. This in turn can also cause negative effects, for instance severe delays on the underlying road network.
In this talk, we will discuss two improvements. First, a ramp metering algorithm that controls traffic on a microscopic scale. Instead of allowing a flow traffic based on minute-average flow, we develop an algorithm that allows an individual vehicle into the stream whenever a suitable gap is measured. Crucial is the variation in acceleration from the stop line. For this, experimental data are collected and analysed.
Secondly, we will show how influencing lane changes on the motorway upstream of the bottleneck can have the same effect on outflow, and hence total delay. This control principle allows to minmize delays in two different ways, namely by limiting inflow from the onramp and limiting flow on the motorway. This way, a road authority can balance delays from different origins, and still have an optimal outflow. Current efforts include implementing this strategy on the road, providing road users avise via a mobile app.
Prediction of travel time distributions with a Markovian Velocity Model
Despite measures to reduce congestion, occurrences of both recurrent and non-recurrent congestion cause large delays in road networks with important economic implications. Educated use of Intelligent Transportation Systems (ITS) can significantly reduce travel times. In this talk, we will discuss a new stochastic process that incorporates ITS information to model the uncertainties affecting congestion in road networks. A Markov-modulated background process tracks traffic events that affect the speed of travelers. The resulting continuous-time model allows for correlation between velocities on the arcs and incorporates both recurrent and non-recurrent congestion. We will show how the model can be used to predict travel time distributions and, as a result, provide route guidance for travelers.
Additionally, we will discuss how the flexibility of the Markov model can be employed to fit the model with real-life data sets containing loop detector data and lists of reported incidents.
A diffusion-based analysis of a multi-class road traffic network
In this talk I’ll discuss a stochastic model that describes the evolution of vehicle densities in a road network. It is consistent with the class of (deterministic) kinematic wave models, which describe traffic flows on the basis of conservation laws that incorporate the macroscopic fundamental diagram (a functional relationship between vehicle density and flow). The setup used is capable of handling multiple types of vehicle densities, with general macroscopic fundamental diagrams, on a network with arbitrary topology.
Interpreting our system as a spatial population process, it turns out that, under a natural scaling, fluid and diffusion limits can be derived. This means that the vehicle density process can be approximated with a suitable Gaussian process, which yield accurate normal approximations to the joint (in the spatial and temporal sense) vehicle density process. The corresponding means and variances allowing efficient computation by solving specific differential equations, we provide insight into the underlying computational complexity.
Time permitting, I also show how the limit results give rise to an approximation to the vehicles’ travel-time distribution between any given origin and destination pair.
(joint work with Jaap Storm)
A transient horizontal-queue model for urban traffic control
In this talk, we present a queueing model for predicting the transient behaviour of traffic networks for different control settings. By considering horizontal queues with a finite capacity, we are able to estimate the blocking probability and the number of vehicles for each lane in the system. These estimations provide a rich input for optimisation algorithms allowing one to compare the performance of different control settings without using computationally expensive traffic microsimulations.
We also discuss the problem of closing the gap between theoretical models and real-life systems. Using induction loops as an example, we show how traffic sensor data can be used to update model predictions and parameters. Our results are compared with traffic microsimulation data.
High-dimensional traffic control through Bayesian optimization
In this talk, we consider high-dimensional traffic signal control problems that arise in congested metropolitan areas. We focus on the use of high-resolution urban mobility stochastic simulators and formulate the control problems as high-dimensional continuous simulation-based optimization (SO) problems. We discuss the opportunities and challenges of designing SO algorithms for these problems. An important component in high-dimensional problems is the exploration-exploitation tradeoff. We discuss work that has focused on improving the exploitation capabilities of SO algorithms. We then present novel exploration techniques suitable for high-dimensional spaces. We consider a Bayesian optimization setting, and propose the use of a simple analytical traffic model to specify the covariance function of a Gaussian process. We show how this enables the Bayesian optimization method to more efficiently sample in high-dimensional spaces. We present validation experiments on synthetic low-dimensional problems. We then apply the method to a high-dimensional traffic control problem for Midtown Manhattan, in New York City.
Optimization and control methods for combined logistic and traffic management
Logistics trip planning and terminal operations require reliable and predictable travel and handling times. However, in densely populated metropolitan areas, travel times are typically unreliable. Since opportunities for expanding road capacity are limited, advanced joint measures in traffic and logistics systems are required. In this talk some highlights from the ToGRIP project will be presented. We will explain how logistic and traffic data can be combined to make short term predictions of traffic volumes, traffic states and waiting times at terminals and we will also explain what kind of interventions can be taken to improve the performance of the logistic and traffic system. The interventions range from a shift of trucks to the off-peak period, to slot terminal management and route and lane choice advice. The benefits are computed for all the different days in a 3 month period to illustrate how much the benefits can vary over different days.
Towards optimal green times at signalized intersections
Setting traffic light signals is a classical topic in traffic engineering and espcially important in heavy traffic conditions when the available capacity is almost fully utilized and longer queues are inevitably formed. For the fixed-cycle traffic-light queue, an elementary queueing model for one traffic light with cyclic signaling, we obtain heavy-traffic limits that capture the long-term queue behavior. We leverage these limit theorems to obtain sharp performance approximations for one queue in heavy traffic. We show that inserting those heavy-traffic approximations leads to tractable optimization problems and close-to-optimal signal prescriptions. Moreover, we show that the same heavy-traffic type of results obtained for the FCTL queue can be obtained more generally, e.g. for vehicle-actuated traffic lights.
Tradable permits as an alternative to road pricing?
Tradable transport permits are increasingly proposed as a possible alternative to road pricing or taxation, as an economic response to reducing congestion and environmental externalities. Despite this growing interest, no schemes have so far been implemented in practice. Academic studies have suggested that tradable schemes are more cost effective and equitable than conventional fiscal policy instruments, and have been considered and trialled by policy makers, but have many outstanding considerations regarding their development, governance and execution. In this presentation I will introduce the idea of trabale permits in mobility and discuss some recent field studies that have been carried out. These teach us important insights on the design, workings, effectiveness and acceptability of such schemes.
I: Description of Road Traffic Flow
II: Control and Optimization of Road Traffic Flow
This tutorial on Road Traffic flow consists of two parts: the first will be about how to describe road traffic flow, and the second part will be on how to control and optimize it. The description will contain the topics Observations, Moving (mostly Driving), Theory, Noise, while control will be divided into urban and non-urban, and, eventually, more systemic controls.
It is not meant to be exhaustive, which is hardly possible in 90 minutes, but is thought to be a kind of introduction into the topic which will help to understand the more advanced topics to be tackled in this workshop.
A Mathematical Perspective on Multi-class and Multi-lane Modelling
Abstract to be announced
Registration is free, but compulsory.
For ON CAMPUS participation, please follow this link
For ONLINE participation, please follow this link