Gurcan Comert

Queue length estimation from probe vehicle location and the impacts of sample size

Probe vehicles are increasingly receiving more attention as an alternative means of collecting real-time traffic data needed for system optimization. This paper focuses on real-time estimation of queue lengths from the location information of probe vehicles in a queue at an isolated and undersaturated intersection. The paper also addresses the evaluation of the accuracy of such estimates as a function of the market penetration of probe vehicles. An analytical formulation based on conditional probability distributions is developed for estimating the expected queue length and its variance. It is found that, for the given settings, only the location information of the last probe vehicle in the queue is sufficient for the estimation. Exact expressions for the conditional mean and variance of queue length are derived. Various numerical results are documented to show how estimation errors behave by the volume to capacity ratio and by market penetration.

Short-Term Traffic Flow Prediction with Regime Switching Models

Accurate short-term prediction of traffic parameters is a critical component for many intelligent transportation system applications. Traffic flow is subject to abrupt disturbances because of various unexpected events (e.g., accidents, weather-induced disruption) that may change the underlying dynamics and the stability of the data generation process. Short-term prediction models that do not account for these changes produce biased and less accurate predictions. This paper proposes a new adaptive approach to short-term prediction that explicitly accounts for occasional regime changes by using statistical change-point detection algorithms. In this context, the expectation maximization and the CUSUM (cumulative sum) algorithms are implemented to detect shifts in the mean level of the process in real time. Autoregressive integrated moving average models are used for developing the forecasting models while the process mean is monitored by the two detection algorithms. The intercept of the forecasting models is updated on the basis of the detected shifts in the mean level to adapt to any potential new regimes. The proposed approach is tested on real-world loop data sets. The results show significant improvements in prediction accuracy compared with traditional autoregressive integrated moving average models with fixed parameters.

Analytical Evaluation of the Error in Queue Length Estimation at Traffic Signals From Probe Vehicle Data

Probe vehicle data are increasingly becoming more attractive for real-time system state estimation in transportation networks. This paper presents analytical models for the real-time estimation of queue lengths at traffic signals using the fundamental information (i.e., location and time) that probe vehicles provide. For a single queue with Poisson arrivals, analytical models are developed to evaluate how error changes in queue length estimation as the percentage of probe vehicles in the traffic stream varies. When the overflow queue is ignored, a closed-form solution is obtained for the variance of the estimation error. For the more general case with the overflow queue, a formulation for the error variance is presented, which requires the marginal probability distribution of the overflow queue as the input. In addition, an approximate model is presented for the latter case, which yields results that are comparable with the exact solution. Overall, the formulations presented here can be used to assess the error in queue length estimation from probe data without conducting simulation runs for various scenarios of probe vehicle market-penetration rates and congestion levels.

Short-term freeway traffic parameter prediction

Three Grey System theory models for short term traffic speed prediction studied.The grey models demonstrated better accuracy than other tested nonlinear models.The Verhulst model with Fourier error correction demonstrates the best accuracy.The simpler derivations can allow the algorithms to be placed on portable devices.Well-defined mathematics of models can allow alteration for multidimensional data. Intelligent transportation systems applications require accurate and robust prediction of traffic parameters such as speed, travel time, and flow. However, traffic exhibits sudden shifts due to various factors such as weather, accidents, driving characteristics, and demand surges, which adversely affect the performance of the prediction models. This paper studies possible applications and accuracy levels of three Grey System theory models for short-term traffic speed and travel time predictions: first order single variable Grey model (GM(1,1)), GM(1,1) with Fourier error corrections (EFGM), and the Grey Verhulst model with Fourier error corrections (EFGVM). Grey models are tested on datasets from California and Virginia. They are compared to nonlinear time series models. Grey models are found to be simple, adaptive, able to deal better with abrupt parameter changes, and not requiring many data points for prediction updates. Based on the sample data used, Grey models consistently demonstrate lower prediction errors over all the time series improving the accuracy on average about 50% in Root Mean Squared Errors and Mean Absolute Percent Errors.

An Online Change-Point-Based Model for Traffic Parameter Prediction

This paper develops a method for predicting traffic parameters under abrupt changes based on change point models. Traffic parameters such as speed, flow, and density are subject to shifts because of weather, accidents, driving characteristics, etc. An intuitive approach of employing the hidden Markov model (HMM) and the expectation-maximization (EM) algorithm as change point models at these shifts and accordingly adapting the autoregressive-integrated-moving-average (ARIMA) forecasting model is formulated. The model is fitted and tested using publicly available 1993 I-880 loop data. It is compared with basic and mean updating forecasting models. Detailed numerical experiments are given on several days of data to show the impact of using change point models for adaptive forecasting models.

Effect of stop line detection in queue length estimation at traffic signals from probe vehicles data

Stop line detectors are one of the most deployed traffic data collection technologies at signalized intersections today. Newly emerging probe vehicles are increasingly receiving more attention as an alternative means of real-time monitoring for better system operations, however, high market penetration levels are not expected in the near future. This paper focuses on real-time estimation of queue lengths by combining these two data types, i.e., actuation from stop line detectors with location and time information from probe vehicles, at isolated and undersaturated intersections. Using basic principles of statistical point estimation, analytical models are developed for the expected total queue length and its variance at the end of red interval. The study addresses the evaluation of such estimators as a function of the market penetration of probe vehicles. Accuracy of the developed models is compared using a microscopic simulation environment-VISSIM. Various numerical examples are presented to show how estimation errors behave by the inclusion of stop line detection for different volume to capacity ratio and market penetration levels. Results indicate that the addition of stop line detection improves the estimation accuracy as much as 14% when overflow queue is ignored and 24% when overflow queue is included for less than 5% probe penetration level.

Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters

This paper develops estimators for market penetration level and arrival rate in finding queue lengths from probe vehicles at isolated traffic intersections. Closed-form analytical expressions for expectations and variances of these estimators are formulated. Derived estimators are compared based on squared error losses. Effect of number of cycles (i.e., short-term and long-term performances), estimation at low penetration rates, and impact of combinations of derived estimators on queue length problem are also addressed. Fully analytical formulas with unknown parameters are derived to evaluate how queue length estimation errors change with respect to percent of probe vehicles in the traffic stream. Developed models can be used for the real-time cycle-to-cycle estimation of the queue lengths by inputting some of the fundamental information that probe vehicles provide (e.g., location, time, and count). Models are evaluated using VISSIM microscopic simulations with different arrival patterns. Numerical experiments show that the developed estimators are able to point the true arrival rate values at 5% probe penetration level with 10 cycles of data. For low penetrations such as 0.1%, large number of cycles of data is required by arrival rate estimators which are essential for overflow queue and volume-to-capacity ratios. Queue length estimation with tested parameter estimators is able to provide cycle-to-cycle errors within ±5% of coefficient of variations with less than 5 cycles of probe data at 0.1% penetration for all arrival rates used.

Estimating Queues at Signalized Intersections: Value of Location and Time Data from Instrumented Vehicles

Vehicles instrumented with location tracking and wireless communication technologies (i.e., the so called probe vehicles) can serve as sensors for monitoring traffic conditions on transportation links. This paper is focused on estimating queue lengths in real-time at a signalized intersection approach based on the location and time data from probe vehicles that may constitute a given percentage of the total traffic. The paper also addresses the evaluation of the accuracy of such estimates. Using a virtual queuing model and conditional probability distributions, new expressions are derived for the variance of the estimates to understand how accuracy is affected by the percentage of probes in the traffic stream and by the type of information collected, which include (i) location of probes in the queue and (ii) both the location of probes and the times/instances at which they join the back-of-the queue. Numerical examples are presented to compare and contrast the accuracies of these two cases. The findings and the formulation presented in this paper could be used in evaluating and designing a traffic monitoring system that relies on probe vehicle data for queue length estimation at signalized intersections.

Modeling Cyber Attacks at Intelligent Traffic Signals

Transportation networks are considered one of the critical physical infrastructures for resilient cities (cyber-physical systems). In efforts to minimize adverse effects that come with the advancement of vehicular technologies, various governmental agencies, such as the U.S. Department of Homeland Security and the National Highway Traffic Safety Administration (NHTSA), work together. This paper develops belief-network-based attack modeling at signalized traffic networks under connected vehicle and intelligent signals frameworks. For different types of cyber attacks, defined in the literature, risk areas and impacts of attacks are evaluated. Vulnerability scores, technically based on the selected metrics, are calculated for signal controllers. In addition, the effect of having redundant traffic sensing systems on intersection performance measures is demonstrated in terms of average queue length differences.