Mobility Digital Twin with Connected Vehicles and Cloud Computing

<div>A Digital Twin is a digital replica of a living or non-living physical entity, and this emerging technology has attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, by leveraging both connected vehicle technology and cloud computing, an Mobility Digital Twin (MDT) framework is developed, which consists of three building blocks in the physical space (namely Human, Vehicle, and Traffic), and their associated Digital Twins in the digital space. The cloud architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to implement its digital functionalities of storage, modeling, learning, simulation, and prediction. The effectiveness of the MDT framework is shown through the case studies of three digital building blocks with their key microservices: the Human Digital Twin with user management and driver type classification, the Vehicle Digital Twin with cloud-based Advanced Driver-Assistance Systems (ADAS), and the Traffic Digital Twin with traffic flow monitoring and variable speed limit.</div>

Mobility Digital Twin with Connected Vehicles and Cloud Computing

<div>A Digital Twin is a digital replica of a living or non-living physical entity, and this emerging technology has attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, by leveraging both connected vehicle technology and cloud computing, an Mobility Digital Twin (MDT) framework is developed, which consists of three building blocks in the physical space (namely Human, Vehicle, and Traffic), and their associated Digital Twins in the digital space. The cloud architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to implement its digital functionalities of storage, modeling, learning, simulation, and prediction. The effectiveness of the MDT framework is shown through the case studies of three digital building blocks with their key microservices: the Human Digital Twin with user management and driver type classification, the Vehicle Digital Twin with cloud-based Advanced Driver-Assistance Systems (ADAS), and the Traffic Digital Twin with traffic flow monitoring and variable speed limit.</div>

Driving Confidence in a Connected Vehicle Environment: A Case Study of Expressway Work Zone

At present, how the application of connected vehicle technology will affect drivers’ driving psychology needs to be explored. As an important part of driving psychology, driving confidence can guide drivers to operate calmly when facing a complex traffic environment, which has an important impact on reducing accident rates and improving traffic efficiency. Based on the driving behavior data in the expressway work zone under a connected vehicle environment, this study mainly analyzed the difference between the psychological characteristics of drivers with warning information or without warning information when facing the work zone ahead. Firstly, based on driving simulation technology, the expressway work zone scene in a connected vehicle environment was designed, and the on-board human-machine interface was used to provide warning information of the work zone ahead. Secondly, the difference of drivers’ driving confidence in psychology when driving with or without warning information was analyzed by using the characteristics of average vehicle spatiotemporal diagram and gas pedal angle. Finally, a method of quantifying driving confidence was proposed, which used a kind of objective weighting method to get the weights between different indicators. Based on this method, drivers’ degree of driving confidence under two conditions was calculated. The results showed that connected vehicle technology could affect drivers’ driving confidence in psychology when facing the work zone ahead. In the connected vehicle environment, 82.9% of drivers’ degree of driving confidence would increase, and the average degree of driving confidence with warning information was 10.9% higher than that without warning information.

An Empirical Study on GAN-Based Traffic Congestion Attack Analysis: A Visualized Method

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.

Microscopic Estimation of Freeway Vehicle Positions Using Mobile Sensors

The introduction of mobile sensors, i.e. probe vehicles with GPS-enabled smart phones or connected vehicle technology, will potentially provide more comprehensive information on roadway conditions than conventional point detection alone. Several mobility applications have been proposed that utilize this new vehicle-specific data rather than aggregated speed, density, and flow. Because of bandwidth limitations of cellular and an expected slow deployment of connected vehicles, only a portion of vehicles on the roadway will be able to report their positions at any given time. This paper proposes a novel technique to analyze the behavior of freeway vehicles equipped with GPS receivers and accelerometers to estimate the quantity, locations, and speeds of those vehicles that do not have similar equipment. If an equipped vehicle deviates significantly from a car-following model’s expected behavior, the deviation is assumed to be the result of an interaction with an unequipped vehicle (i.e. an undetectable “ghost” vehicle). This unequipped vehicle is then inserted into a rolling estimation of individual vehicle movements. Because this technique is dependent on vehicles interacting during congestion, a second scenario uses an upstream detector to detect and insert unequipped vehicles at the point of detection, essentially “seeding” the network. An evaluation using the NGSIM US-101 dataset shows realistic vehicle density estimations during and immediately after congestion. Introducing an upstream detector to supply initial locations of unequipped vehicles improves accuracy in free flow conditions, thereby improving the root mean squared error of the number of vehicles within a 120-foot cell from 3.8 vehicles without a detector, to 2.4 vehicles with a detector, as compared to ground truth.

Microscopic Estimation of Arterial Vehicle Positions in a Low-Penetration-Rate Connected Vehicle Environment

Wireless communication among vehicles and roadside infrastructure, known as connected vehicles, is expected to provide higher-resolution real-time vehicle data, which will allow more effective traffic monitoring and control. Availability of connected vehicle technology among the vehicle fleet will likely grow gradually, and possibly remain limited, with many drivers potentially being unwilling to transmit their locations. This is problematic given that research has indicated that the effectiveness of many connected vehicle mobility applications will be dependent on the availability of location data from a minimum of 20-30% of roadway vehicles. In an effort to improve the performance of connected vehicle applications at low connected vehicle technology penetration rates, we propose a novel technique to estimate the positions of non-communicating (unequipped) vehicles based on the behaviors of communicating (equipped) vehicles along a signalized arterial. Unequipped vehicle positions are estimated based on observed gaps in a stopped queue, and the forward movement of these estimated vehicles are simulated microscopically using a commercial traffic simulation software package. In simulations, the algorithm made more correct than incorrect estimates of unequipped vehicle positions in the same lane and within 7 meters longitudinally. When applied to a previously-developed connected vehicle traffic signal control strategy in simulation, the location estimation algorithm produced small improvements in delays, speeds, and stopped delay when compared to an equipped vehicle-only scenario at penetration rates of 25% or less. The location estimation algorithm is generic, and could be applied to other connected vehicle applications to improve performance at low penetration rates.