35 research outputs found
An Area-Aggregated Dynamic Traffic Simulation Model
Microscopic and macroscopic dynamic traffic models not fast enough to run in an optimization loop to coordinate traffic measures over areas of twice a trip length (50x50 km). Moreover, in strategic planning there are models with a spatial high level of detail, but lacking the features of traffic dynamics. This paper introduces the Network Transmission Model (NTM), a model based on areas, exploiting the Macroscopic or Network Fundamental Diagram (NFD). For the first time, a full operational model is proposed which can be implemented in a network divided into multiple subnetworks, and the physical properties of spillback of traffic jams for subnetwork to subnetwork is ensured. The proposed model calculates the traffic flow between to cell as the minimum of the demand in the origin cell and the supply in the destination cell. The demand first increasing and then decreasing as function of the accumulation in the cell; the supply is first constant and then decreasing as function of the accumulation. Moreover, demand over the boundaries of two cells is restricted by a capacity. This system ensures that traffic characteristics move forward in free flow, congestion moves backward and the NFD is conserved. Adding the capacity gives qualitatively reasonable effects of inhomogeneity. The model applied on a test case with multiple destinations, and re-routing and perimeter control are tested as control measures
The Effect of Crosswalks on Traffic Flow
In urban areas and especially in inner cities, pedestrians crossing the road considerably influence the road traffic flow. For political reasons, priority could be given to pedestrians. A larger number of crossings reduces the pedestrian load per crossing and facilitates both the pedestrian flow and the car flow; the ultimate case is a “cross anywhere” scenario. Earlier work shows that the road capacity decreases with the square of the pedestrian crossing time, hence a short crossing time is desired. Crosswalks can ensure pedestrians cross orthogonally, and thus quickly, and can thereby improve traffic flow. Moreover, a limited number of crosswalks is less stressful than a “cross anywhere” scenario for a car driver since (s)he only needs to expect crossing pedestrians at dedicated crosswalks. This paper studies the effect of the distances between crosswalk and road traffic capacity. The paper’s goal is finding a single formula or universal set of charts that can describe the effect of pedestrian crosswalks on traffic flow under virtually all scenarios (with long blocks). This type of result would obviate the need for simulations of specific situations when only a rough assessment of the effect of crosswalks is desired. Traffic flow for several distances between pedestrian crossings is simulated, and moreover, a non-constant inter-crosswalk spacing is considered. The simulation results can be used for other situations, using transformations and an interpolation recipe. Overall, the closer the crosswalks, the better the flow. However, spacings closer than approximately 25-50 meters do not add much. Speed of traffic under a broad array of pedestrian crossing scenarios is given
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.Comment: 13 pages, 5 figure
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
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.Comment: 6 pages, 9 figure