Mecit Cetin

Modeling traffic signal control using Petri nets

This paper focuses on the use of Petri nets (PN) to model the control of signalized intersections. The application of PN to an eight-phase traffic signal controller is illustrated. Structural analysis of the control PN model is performed to demonstrate how the model enforces the traffic operation safety rules. This is followed by a discussion of why this modeling tool has future value as the use of more advanced control strategies continue to expand.

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.

A direct redistribution model of congestion pricing

This paper discusses a direct redistribution approach to congestion pricing in which monies collected from drivers on a more desirable route are directly transferred to users on a less desirable route. An analytical model for a two-node two-route network is developed. An example is used to demonstrate the applicability of this model. It is shown that this model of toll collection and subsidization will reduce the travel cost for all travelers and totally eliminate the waiting time in the queue. When compared against the social optimal assignment, the direct redistribution model yields almost identical results.

Factors Affecting Minimum Number of Probes Required for Reliable Estimation of Travel Time

Using probe vehicles rather than other detection technologies has great value, especially when travel time information is sought in a transportation network. Even though probes enable direct measurement of travel times across links, the quality or reliability of a system state estimate based on such measurements depends heavily on the number of probe observations across time and space. Clearly, it is important to know what level of travel time reliability can be achieved from a given number of probes. It is equally important to find ways (other than increasing the sample size of probes) of improving the reliability in the travel time estimate. This paper provides two new perspectives on those topics. First, the probe estimation problem is formulated in the context of estimating travel times. Second, a method is introduced to create a virtual network by inserting dummy nodes in the midpoints of links to enhance the ability to estimate travel times further in a way that is more consistent with the processing that vehicles receive. Numerical experiments are presented to illustrate the value of those ideas.

Estimating Queue Dynamics at Signalized Intersections from Probe Vehicle Data: Methodology Based on Kinematic Wave Model

As vehicle-to-vehicle and vehicle-to-infrastructure communications technologies are evolving, data from vehicles equipped with location and wireless technologies provide new opportunities to observe traffic flow dynamics more precisely. A new methodology is presented for estimating the dynamics of vehicular queues at signalized intersections on the basis of the event data generated by probe vehicles. The methodology uses shock wave theory (i.e., the Lighthill–Whitham–Richards theory) to estimate the evolution of the back of the queue over time and space from the event data generated when probe vehicles join the back of the queue. The time and space coordinates of these events are used to develop a formulation for determining the critical points needed to create time–space diagrams or shock waves to characterize the queue dynamics. The methodology is applied to sample data generated from the microscopic traffic simulation software VISSIM. It is found that the proposed methodology is effective in estimating queue dynamics at traffic signals.

Numerical Characterization of Gross Vehicle Weight Distributions from Weigh-in-Motion Data

This paper presents a method for quantifying gross vehicle weight (GVW) distributions of commercial vehicles using weigh-in-motion (WIM) data. Finite mixture models are used to fit a combination of normal distributions to the overall GVW distribution to identify peak parameters more precisely. The GVW distribution is commonly bimodal or trimodal with prominent peaks occurring in the loaded and unloaded weight ranges. The GVW characteristics of FHWA Class 9 vehicles are commonly used for assessing WIM accuracy by visual interpretation of frequency histograms. Temporal changes in the GVW distribution are difficult to detect using common visualization techniques. Mixture models enable the statistical identification of the modal peaks and the proportion of traffic belonging to those peaks for ongoing monitoring purposes. Mixture models are applied to a WIM site in Indiana to illustrate the analysis method and benefits. Numerical monitoring of the GVW distribution is shown to have some advantages over the widely accepted metric based on the Class 9 steer axle weight. The proposed metric should not be used singularly, rather as an additional tool to complement existing metrics.

Bayesian Models for Reidentification of Trucks Over Long Distances on the Basis of Axle Measurement Data

Vehicle reidentification methods can be used to anonymously match vehicles crossing 2 different locations on the basis of vehicle attribute data. In this article, reidentification methods are developed to match commercial vehicles that cross 2 weigh-in-motion sites in Oregon that are separated by 145 miles. Using vehicle length and axle data as attributes to characterize vehicles, a Bayesian model is developed that uses probability density functions obtained by fitting Gaussian mixture models to a sample data set of matched vehicles. The reidentification model when applied to a test data set (where each downstream vehicle also crosses the upstream site) matches vehicles with an accuracy of 91% when both axle weight and axle spacings data are used. To account for the fact that not all vehicles in the downstream also cross the upstream site, an additional new step is developed to screen mismatched vehicles produced by the algorithm. For this step, several screening methods are developed that allow the user to trade off the total number of matched vehicles and error rate. For evaluating the effectiveness of the screening methods, 2 scenarios are considered. In the first scenario, only common vehicles that cross both the upstream and downstream sites are considered, whereas in the second scenario all downstream vehicles are considered. It is shown that the mismatch error can be reduced to as low as 1% and 5% at the expense of not matching about 25% of the common vehicles (crossing both sites) for the first and second scenarios, respectively.

Estimating Traffic Flow Rate on Freeways from Probe Vehicle Data and Fundamental Diagram

Traffic flow rate information obtained from loop detectors is important for many traffic management applications. Given the installation and maintenance costs of such detectors is high, many transportation agencies are shifting to probe vehicle based traffic data. However, estimating traffic flow rates from probe vehicle data remains critical. This paper attempts to estimate traffic flow rates by utilizing a well-calibrated fundamental diagram in combination with the traffic speed information obtained from the probe vehicle. Different single-regime fundamental diagrams and aggregation intervals of the probe vehicle data are investigated in search of the combination that provides the most accurate estimate of flow rates. The results suggest that flow rates are best estimated by using fundamental diagram developed by Van Aerde. Moreover, estimates of flow rates during congested periods are found to be more accurate than free-flow periods.