arXiv subjects

Gurcan Comert

Publications and source records attributed to Gurcan Comert.

47 records · Page 3Linked to original sources

arXiv2020

Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks

This study presents a methodology to quantify vulnerability of cyber attacks and their impacts based on probabilistic graphical models for intelligent transportation systems under connected and autonomous vehicles framework. Cyber attack vulnerabilities from various types and their impacts are calculated for intelligent signals and cooperative adaptive cruise control (CACC) applications based on the selected performance measures. Numerical examples are given that show impact of vulnerabilities in terms of average intersection queue lengths, number of stops, average speed, and delays. At a signalized network with and without redundant systems, vulnerability can increase average queues and delays by $3\%$ and $15\%$ and $4\%$ and $17\%$, respectively. For CACC application, impact levels reach to $50\%$ delay difference on average when low amount of speed information is perturbed. When significantly different speed characteristics are inserted by an attacker, delay difference increases beyond $100\%$ of normal traffic conditions.

cs.CR↗

arXiv2020

Improved Grey System Models for Predicting Traffic Parameters

In transportation applications such as real-time route guidance, ramp metering, congestion pricing and special events traffic management, accurate short-term traffic flow prediction is needed. For this purpose, this paper proposes several novel \textit{online} Grey system models (GM): GM(1,1$|cos(ωt)$), GM(1,1$|sin(ωt)$, $cos(ωt)$), and GM(1,1$|e^{-at}$,$sin(ωt)$,$cos(ωt)$). To evaluate the performance of the proposed models, they are compared against a set of benchmark models: GM(1,1) model, Grey Verhulst models with and without Fourier error corrections, linear time series model, and nonlinear time series model. The evaluation is performed using loop detector and probe vehicle data from California, Virginia, and Oregon. Among the benchmark models, the error corrected Grey Verhulst model with Fourier outperformed the GM(1,1) model, linear time series, and non-linear time series models. In turn, the three proposed models, GM(1,1$|cos(ωt)$), GM(1,1$|sin(ωt)$,$cos(ωt)$), and GM(1,1$|e^{-at}$,$sin(ωt)$,$cos(ωt)$), outperformed the Grey Verhulst model in prediction by at least $65\%$, $16\%$, and $11\%$, in terms of Root Mean Squared Error, and by $82\%$, $58\%$, and $42\%$, in terms of Mean Absolute Percentage Error, respectively. It is observed that the proposed Grey system models are more adaptive to location (e.g., perform well for all roadway types) and traffic parameters (e.g., speed, travel time, occupancy, and volume), and they do not require as many data points for training (4 observations are found to be sufficient).

stat.AP↗

arXiv2020

A Simple Traffic Signal Control Using Queue Length Information

Developments in sensor technologies, especially emerging connected and autonomous vehicles, facilitate better queue length (QL) measurements on signalized intersection approaches in real time. Currently there are very limited methods that utilize QL information in real-time to enhance the performance of signalized intersections. In this paper we present methods for QL estimation and a control algorithm that adjusts maximum green times in actuated signals at each cycle based on QLs. The proposed method is implemented at a single intersection with random and platoon arrivals, and evaluated in VISSIM (a microscopic traffic simulation environment) assuming 100 % accurate cycle-by-cycle queue length information is available. To test the robustness of the method, numerical experiments are performed where traffic demand is increased and by 20\% relative to the demand levels for which signal timing parameters are optimized. Compared to the typical fully-actuated signal control, the proposed QL-based method improves average delay, number of stops, and QL for random arrivals, by 6 %, 9 %, and 10 % respectively. In addition, the method improves average delay, number of stops, and QL by 3 %, 3 %, and 11 % respectively for platoon vehicle arrivals.

eess.SY↗

arXiv2020

Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment

Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure, and traffic management centers. However, it is a challenge to detect security threats in real-time and develop appropriate or effective countermeasures for a CV system because of the dynamic behavior of such attacks, high computational power requirement, and a historical data requirement for training detection models. To address these challenges, statistical models, especially change point models, have potentials for real-time anomaly detections. Thus, the objective of this study is to investigate the efficacy of two change point models, Expectation Maximization (EM) and two forms of Cumulative Summation (CUSUM) algorithms (i.e., typical and adaptive), for real-time V2I cyber attack detection in a CV Environment. To prove the efficacy of these models, we evaluated these two models for three different type of cyber attack, denial of service (DOS), impersonation, and false information, using basic safety messages (BSMs) generated from CVs through simulation. Results from numerical analysis revealed that EM, CUSUM, and adaptive CUSUM could detect these cyber attacks, DOS, impersonation, and false information, with an accuracy of (99%, 100%, 100%), (98%, 10%, 100%), and (100%, 98%, 100%) respectively.

cs.CR↗

arXiv2020

Flow Rate Estimation From Probe Vehicle Data And Sample Size Requirements

The interest to use probe vehicles for traffic monitoring is growing. This paper is focused on the estimation of flow rate from probe vehicle data and the evaluation of sample size requirements. Three cases are considered depending on the available information on the percentage of probes p and the flow rate λ: i) p is known but λis unknown ii) λis known but p is unknown, and iii) both parameters are not known. Estimation methods for all three cases are presented along with the reliability of these estimates. For the first two cases, count data provide sufficient information to estimate the unknown parameters p and λindividually when the other one is known. For these two cases, simple analytical expressions are derived to analyze the accuracy and sample size requirements. However, when both parameters are unknown then additional information beyond count data is required. The position of probe vehicles in a queue at signalized intersections is used as additional information in that case. The results show for how long data need to be collected to estimate the parameters at acceptable confidence levels.

stat.AP↗

arXiv2020

Computational Fluid Dynamic Simulations In a Model of a Carotid Bifurcation Under Steady Flow Conditions

Strokes are still one of the leading causes for death after heart diseases and cancer in all over the world. Most strokes happen because an artery that carries blood uphill from the heart to the head gets clogged. Most of the time, as with heart attacks, the problem is atherosclerosis, hardening of the arteries, calcified buildup of fatty deposits on the vessel wall. The primary troublemaker is the carotid artery, one on each side of the neck, the main thoroughfare for blood to the brain. Only within the last 25 years, though, have researchers been able to put their finger on why the carotid is especially susceptible to atherosclerosis. In this study, the fluid dynamic simulations were done in a carotid bifurcation under the steady flow conditions computationally. In vivo geometry and boundary conditions were obtained from a diseased who has both sides of stenosis located in his carotid artery patients. The location of critical flow fields such as low wall shear stress (WSS), stagnation regions and separation regions were detected. Low WSS was found at the downstream of the bifurcation.

q-bio.TO↗

arXiv2020

Analysis of Queue Length Prediction from Probe Vehicles Problem with Bunch Arrival Headways

This paper discusses the real-time prediction of queue lengths from probe vehicles for the Bunch arrival headways at an isolated intersection for undersaturated conditions. The paper incorporates the bunching effect of the traffic into the evaluation of the accuracy of the predictions as a function of proportion of probe vehicles to entire vehicle population. Formulations are presented for predicting the expected queue length and its variance based on Negative Exponential and Bunched Exponential vehicle headways. Numerical results for both vehicle headway types are documented to show how prediction errors behave by the volume to capacity ratio and probe proportions. It is found that the Poisson arrivals generate conservative confidence intervals and demand higher probe proportions compared to Bunched Exponential headways at the same arrival rate and probe proportion.

stat.AP↗

arXiv2019

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the patterns in real-time in order to reduce congestion. Real-time prediction of queue lengths can be used to adjust the phasing and timings for different movements at an intersection with ASCS. The accuracy of the prediction varies based on the factors, such as the stochastic nature of the vehicle arrival rates, time of the day, weather and driver characteristics. In addition, accurate prediction for multilane, undersaturated and saturated traffic scenarios is challenging. Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS using four variations of Grey systems: (i) the first order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections. The efficacy of the GM is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they are able to adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with ASCS on a calibrated roadway network in Lexington, South Carolina. GM were compared with linear, nonlinear time series models, and long short-term memory (LSTM) neural network. Based on our analyses, we found that EGVM reduces the prediction error over closest competing models (i.e., LSTM and time series models) in predicting average and maximum queue lengths by 40% and 42%, respectively, in terms of Root Mean Squared Error, and 51% and 50%, respectively, in terms of Mean Absolute Error.

eess.SY↗

arXiv2019

Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections

Although Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection, this safety is hindered as pedestrians often do not carry hand-held devices (e.g., Dedicated short-range communication (DSRC) and 5G enabled cell phone) to communicate with connected vehicles nearby. To overcome this limitation, in this study, traffic cameras at a signalized intersection were used to accurately detect and locate pedestrians via a vision-based deep learning technique to generate safety alerts in real-time about possible conflicts between vehicles and pedestrians. The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds). We develop a pedestrian alert safety system (PASS) to generate a safety alert of an imminent pedestrian-vehicle crash using generated PSMs to improve pedestrian safety at a signalized intersection. Our approach estimates the location and velocity of a pedestrian more accurately than existing DSRC-enabled pedestrian hand-held devices. A connected vehicle application, the Pedestrian in Signalized Crosswalk Warning (PSCW), was developed to evaluate the vision-based PASS. Numerical analyses show that our vision-based PASS is able to satisfy the accuracy and latency requirements of pedestrian safety applications in a connected vehicle environment.

cs.CV↗

arXiv2018

Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment

Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure and traffic management centers. However, it is a challenge to detect security threats in real-time and develop appropriate/effective countermeasures for a CV system because of the dynamic behavior of such attacks, high computational power requirement and a historical data requirement for training detection models. To address these challenges, statistical models, especially change point models, have potentials for real-time anomaly detections. Thus, the objective of this study is to investigate the efficacy of two change point models, Expectation Maximization (EM) and Cumulative Sum (CUSUM), for real-time V2I cyber attack detection in a CV Environment. To prove the efficacy of these models, we evaluated these two models for three different type of cyber attack, denial of service (DOS), impersonation, and false information, using basic safety messages (BSMs) generated from CVs through simulation. Results from numerical analysis revealed that EM and CUSUM could detect these cyber attacks, DOS, impersonation, and false information, with an accuracy of 99\%, 100\%, and 98\%, and 100\%, 100\% and 98\%, respectively.

cs.CR↗

arXiv2018

Towards Secure Infrastructure-based Cooperative Adaptive Cruise Control

Cooperative Adaptive Cruise Control (CACC) is a pivotal vehicular application that would allow transportation field to achieve its goals of increased traffic throughput and roadway capacity. This application is of paramount interest to the vehicular technology community with a large body of literature dedicated to research within different aspects of CACC, including but not limited to security with CACC. Of all available literature, the overwhelming focus in on CACC utilizing vehicle-to-vehicle (V2V) communication. In this work, we assert that a qualitative increase in vehicle-to-infrastructure (V2I) and infrastructure-to-vehicle (I2V) involvement has the potential to add greater value to CACC. In this study, we developed a strategy for detection of a denial-of-service (DoS) attack on a CACC platoon where the system edge in the vehicular network plays a central role in attack detection. The proposed security strategy is substantiated with a simulation-based evaluation using the ns-3 discrete event network simulator. Empirical evidence obtained through simulation-based results illustrate successful detection of the DoS attack at four different levels of attack severity using this security strategy.

cs.CR↗