arXiv subjects

Victor L. Knoop

Publications and source records attributed to Victor L. Knoop.

8 recordsLinked to original sources

arXiv2026

Calming traffic in unsignalized bidirectional streets by introducing one-lane bottlenecks

A common strategy to calm traffic in an unsignalized, bidirectional street consists in narrowing the street to a single lane at one or more locations. These locations become bottlenecks. Two policies are commonly used there: first-in-first-out (FIFO), and directional priority (DP), which means one of the directions has priority. This paper examines the performance of these two policies on streets with one or more bottlenecks. Two measures of performance are considered: the expected delay (evaluated for typical situations with low demand) and the system capacity. The latter is defined as the pair of flows (d1, d2) that depart the street in the two opposite directions when the system is oversaturated with queues. For a street with a single bottleneck, the capacity under both FIFO and DP is not unique: In both cases, the values of (d1, d2) form a continuous decreasing curve. As expected, we find that arrival flow pairs (a1, a2) below this curve can get through the system without generating queues. Those above cannot. For a single bottleneck it is shown that FIFO yields lower expected delay than DP under low demand. For high demand, DP's capacity exceeds FIFO's when the flows are balanced, but are lower otherwise. For multiple bottlenecks, the expected total delay on the street under low demand is that of a single bottleneck multiplied by the number of bottlenecks. The multi-bottleneck capacity under FIFO turns out to be the same as for a single bottleneck. For DP, traffic near capacity is complex. Capacity points turn out to be numerous and disjointed. Formulas are derived for their values. Higher capacities are typically obtained for longer streets. Curiously, these capacity points are often above the capacity curve of a street with a single FIFO or DP bottleneck, indicating that an extra bottleneck can increase capacity.

physics.soc-ph↗

arXiv2026

On Traffic Interactions for Unmanned Aerial Vehicles: Traffic Flow Applied to Three Dimensional Space

Unmanned aerial vehicles (UAVs, or drones) are likely to significantly increase the amount of air traffic. If the skies are full of UAVs, they need to interact with each other, for instance by yielding or other evasive maneuvers. The aggregated movements of drones will create traffic patterns. Just like in current road traffic, the interactions will be very frequent, so a centralized computer managing these interactions is expected not to be possible. There is a long history of traffic flow theory and modeling for 1 dimensional (road) traffic; this has been expanded to 2 dimensional traffic (pedestrians). It is unclear how traffic flow theory works for 3 dimensional traffic. In this paper we show how drone traffic can interact in a decentralized way. For the microscopic description, we add asymmetric interaction rules. We show that without centralized control, we can have efficient and safe traffic. Moreover, we provide a framework that directly links microscopic interactions to macroscopic properties. For the macroscopic description, we formulate and apply a numerical scheme that integrates the competition of space by UAVs for multiple classes, directions and dimensions. We apply both the microscopic and macroscopic descriptions to analyze (emerging) patterns which may arise in 3D traffic flow. The current paper provides background to develop interaction rules for drone traffic. Currently, the drone traffic is taking its first steps, but once the aeronautic technique takes off, the legislation regarding drone interactions should be ready. To support so, and be able to assess traffic consequences of decisions, the traffic flow theory framework developed here is essential.

physics.soc-ph↗

arXiv2026

How vehicles change lanes after encountering crashes: Empirical analysis and modeling

When a traffic crash occurs, following vehicles need to change lanes to bypass the obstruction. We define these maneuvers as post crash lane changes. In such scenarios, vehicles in the target lane may refuse to yield even after the lane change has already begun, increasing the complexity and crash risk of post crash LCs. However, the behavioral characteristics and motion patterns of post crash LCs remain unknown. To address this gap, we construct a post crash LC dataset by extracting vehicle trajectories from drone videos captured after crashes. Our empirical analysis reveals that, compared to mandatory LCs (MLCs) and discretionary LCs (DLCs), post crash LCs exhibit longer durations, lower insertion speeds, and higher crash risks. Notably, 79.4% of post crash LCs involve at least one instance of non yielding behavior from the new follower, compared to 21.7% for DLCs and 28.6% for MLCs. Building on these findings, we develop a novel trajectory prediction framework for post crash LCs. At its core is a graph based attention module that explicitly models yielding behavior as an auxiliary interaction aware task. This module is designed to guide both a conditional variational autoencoder and a Transformer based decoder to predict the lane changer's trajectory. By incorporating the interaction aware module, our model outperforms existing baselines in trajectory prediction performance by more than 10% in both average displacement error and final displacement error across different prediction horizons. Moreover, our model provides more reliable crash risk analysis by reducing false crash rates and improving conflict prediction accuracy. Finally, we validate the model's transferability using additional post crash LC datasets collected from different sites.

cs.AI↗

arXiv2023

Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles

Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Autonomous Vehicles (AVs). However, trajectories collected by AVs are noisy and not readily applicable for studying CF behaviour. This paper extracts and enhances two categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset. First, CF pairs are selected based on specific rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the raw CF data is corrected and enhanced via motion planning, Kalman filtering, and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following segments are obtained, with a total driving distance of 150k+ km. A diversity assessment shows that the processed data cover complete CF regimes for calibrating CF models. This open and ready-to-use dataset provides the opportunity to investigate the CF behaviours of following AVs vs. HVs from real-world data. It can further facilitate studies on exploring the impact of AVs on mixed urban traffic.

eess.SY↗

arXiv2023

Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data?

Network-level traffic condition forecasting has been intensively studied for decades. Although prediction accuracy has been continuously improved with emerging deep learning models and ever-expanding traffic data, traffic forecasting still faces many challenges in practice. These challenges include the robustness of data-driven models, the inherent unpredictability of traffic dynamics, and whether further improvement of traffic forecasting requires more sensor data. In this paper, we focus on this latter question and particularly on data from loop detectors. To answer this, we propose an uncertainty-aware traffic forecasting framework to explore how many samples of loop data are truly effective for training forecasting models. Firstly, the model design combines traffic flow theory with graph neural networks, ensuring the robustness of prediction and uncertainty quantification. Secondly, evidential learning is employed to quantify different sources of uncertainty in a single pass. The estimated uncertainty is used to "distil" the essence of the dataset that sufficiently covers the information content. Results from a case study of a highway network around Amsterdam show that, from 2018 to 2021, more than 80\% of the data during daytime can be removed. The remaining 20\% samples have equal prediction power for training models. This result suggests that indeed large traffic datasets can be subdivided into significantly smaller but equally informative datasets. From these findings, we conclude that the proposed methodology proves valuable in evaluating large traffic datasets' true information content. Further extensions, such as extracting smaller, spatially non-redundant datasets, are possible with this method.

cs.LG↗

arXiv2023

Macroscopic analysis and modelling of multi-class, flexible-lane traffic

An excessive demand of vehicles to a motorway bottleneck leads to traffic jams. Motorbikes are narrow and can drive next to each other in a lane, or in-between lanes in low speeds. This paper analyses the resulting traffic characteristics and presents numerical scheme for a macroscopic traffic flow model for these two classes. The behavior included is as follows. If there are two motorbikes behind each other, they can travel next to each other in one lane, occupying the space of one car. Also, at low speeds of car traffic, they can go in between the main lanes, creating a so-called filtering lane. The paper numerically derives functions of class-specific speeds as function of the density of both classes, incorporating flexible lane usage dependent on the speed. The roadway capacity as function of the motorbike fraction is derived, which interesting can be in different types of phases (with motorbikes at higher speeds or not). We also present a numerical scheme to analyse the dynamics of this multi-class system. We apply the model to an example case, revealing the properties of the traffic stream , queue dynamics and class specific travel times. The model can help in showing the relative advantage in travel time of switching to a motorbike.

physics.soc-ph↗

arXiv2020

Estimating the urban traffic state with limited traffic data using the MFD

Urbanization leads to an increase of traffic in cities. The Macroscopic Fundamental Diagram (MFD) suggests to describe urban traffic at a zonal level, in order to measure and control traffic. However, for a proper estimation, all data needs to be available. The main question discussed in this paper is: How to derive a network-wide traffic state estimate? We follow up on literature suggesting to base the operational estimate on the speed of limited sample cars sharing floating car data (FCD). We propose an initial step by constructing an MFD based on FCD, which is then used in step 2, the operational traffic state estimation. For operational traffic state estimation, i.e., the real-time traffic state estimation, the penetration rate is unknown. For both steps, we assess the impact of errors in the estimation. In light of the errors, we also formulate an indicator which shows when the method would yield insensible results, for instance in case of an incident. The method has been tested using microsimulation. A 26% error in the estimated average density is found for a FCD penetration rate of 1%; increasing the penetration rate to 30\% reduces the error in estimated average density to 7%.

physics.soc-ph↗

arXiv2019

Electric Vehicles and their Effect on Network Load

The composition of the fleet of road cars is changing from fuel powered cars to plug-in hybrid (PHEV) and electrical vehicles (EV). The (electrical) range of these vehicles is limited, leading to so called `range anxiety'. Firstly, this leads to a preference of shorter routes. Secondly, (PH)EVs have the ability to regenerate energy from braking, which make routes with many accerelations and decelerations not as unattractive for drivers of (PH)EVs as for drivers of vehicles with an internal combustion engine (ICE). This paper combines this, and adds drivers might prefer a lower speed limit due to a lower energy consumption. These elements are compared to with road characteristics. It is found, motorways are usually less favourable for drivers of (PH)EVs compared to drivers of ICE vehicles, and they will prefer the shorter routes on secondary roads or through towns. This will influence the use of the underlying road network, and thereby affecting congestion, emissions and safety. This shift towards the underlying road network needs to be taken into account in designs for the network.

physics.soc-ph↗