Related papers: Study on Origins of Reverse Lane Usage using Nagel…
This paper presents a mixed traffic control policy designed to optimize traffic efficiency across diverse road topologies, addressing issues of congestion prevalent in urban environments. A model-free reinforcement learning (RL) approach is…
In the behavioral cloning approach to end-to-end driving, a dataset of expert driving is collected and the model learns to guess what the expert would do in different situations. Situations are summarized in observations and the outputs are…
This paper presents an on-board advance warning system for vehicles based on a probabilistic prediction model that advises them on when to change lanes to reach a highway diverge on time. The system is based on a model that estimates the…
The lane reversal has proven to be a useful method to mitigate traffic congestion during rush hour or in case of specific events that affect high traffic volumes. In this work we propose a methodology that is placed within optimization via…
We study kinetic models for traffic flow characterized by the property of producing backward propagating waves. These waves may be identified with the phenomenon of stop-and-go waves typically observed on highways. In particular, a refined…
A generalized optimal velocity model is analyzed, where the optimal velocity function depends not only on the headway of each car but also the headway of the immediately preceding one. The stability condition of the model is derived by…
Vehicle velocities in neighbouring road sections are correlated with memory effects. We explore the response of the velocities in the sequence of sections to a congestion in a given section and its dynamic characteristics. To this end, we…
We have carried out car-following experiments with a 25-car-platoon on an open road section to study the relation between a car's speed and its spacing under various traffic conditions, in the hope to resolve a controversy surrounding this…
Traffic congestion represents a complex urban phenomenon that has been the subject of extensive research employing various modeling techniques grounded in the principles of physics and molecular theory. Although factors such as road design,…
In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are…
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions. Among them, improving the urban transportation efficiency is one of the most prominent topics. Recent…
I study the critical behavior of a traffic model with an absorbing state. The model is a variant of the Nagel-Schreckenberg (NS) model, in which drivers do not decelerate if their speed is smaller than their headway, the number of empty…
The relation between the fundamental observables of traffic flow (i.e., vehicle density, flow rate, and average velocity) is of great importance for the study of traffic phenomena. Probably the most common source of such data are inductive…
A statistical predictive model in which a high-dimensional time-series regenerates at the end of each day is used to model road traffic. Due to the regeneration, prediction is based on a daily modeling using a vector autoregressive model…
This research aims to evaluate the performance of the rotors and study the behavior of the human driver in interacting with the rotors. In recent years, rotors have been increasingly used between countries due to their safety, capacity, and…
Traditional driving risk potential field model generally assumes uniform vehicle sizes, which fails to reflect the heterogeneity in real-world traffic environment. This study aims to develop an improved model by introducing the vehicle…
Vehicles on highway on-ramps are one of the leading contributors to congestion. In this paper, we propose a prediction framework that predicts the longitudinal trajectories and lane changes (LCs) of vehicles on highway on-ramps and tapers.…
We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of…
Sequence-to-sequence learning naturally has two directions. How to effectively utilize supervision signals from both directions? Existing approaches either require two separate models, or a multitask-learned model but with inferior…
Rush hour and sustained traffic flows in eight cities are studied using the IBM Mega Traffic Simulator to understand the importance of road structures and vehicle acceleration in the prevention of gridlock. Individual cars among the tens of…