19,689 research outputs found
Time-to-Green predictions for fully-actuated signal control systems with supervised learning
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
Belief State Planning for Autonomously Navigating Urban Intersections
Urban intersections represent a complex environment for autonomous vehicles
with many sources of uncertainty. The vehicle must plan in a stochastic
environment with potentially rapid changes in driver behavior. Providing an
efficient strategy to navigate through urban intersections is a difficult task.
This paper frames the problem of navigating unsignalized intersections as a
partially observable Markov decision process (POMDP) and solves it using a
Monte Carlo sampling method. Empirical results in simulation show that the
resulting policy outperforms a threshold-based heuristic strategy on several
relevant metrics that measure both safety and efficiency.Comment: 6 pages, 6 figures, accepted to IV201
High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Road accidents are an important issue of our modern societies, responsible
for millions of deaths and injuries every year in the world. In Quebec only, in
2018, road accidents are responsible for 359 deaths and 33 thousands of
injuries. In this paper, we show how one can leverage open datasets of a city
like Montreal, Canada, to create high-resolution accident prediction models,
using big data analytics. Compared to other studies in road accident
prediction, we have a much higher prediction resolution, i.e., our models
predict the occurrence of an accident within an hour, on road segments defined
by intersections. Such models could be used in the context of road accident
prevention, but also to identify key factors that can lead to a road accident,
and consequently, help elaborate new policies.
We tested various machine learning methods to deal with the severe class
imbalance inherent to accident prediction problems. In particular, we
implemented the Balanced Random Forest algorithm, a variant of the Random
Forest machine learning algorithm in Apache Spark. Interestingly, we found that
in our case, Balanced Random Forest does not perform significantly better than
Random Forest.
Experimental results show that 85% of road vehicle collisions are detected by
our model with a false positive rate of 13%. The examples identified as
positive are likely to correspond to high-risk situations. In addition, we
identify the most important predictors of vehicle collisions for the area of
Montreal: the count of accidents on the same road segment during previous
years, the temperature, the day of the year, the hour and the visibility
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