Jonathan Corey

Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression.

Two types of animal-vehicle collision (AVC) data are commonly adopted for AVC-related risk analysis research: reported AVC data and carcass removal data. One issue with these two data sets is that they were found to have significant discrepancies by previous studies. In order to model these two types of data together and provide a better understanding of highway AVCs, this study adopts a diagonal inflated bivariate Poisson regression method, an inflated version of bivariate Poisson regression model, to fit the reported AVC and carcass removal data sets collected in Washington State during 2002-2006. The diagonal inflated bivariate Poisson model not only can model paired data with correlation, but also handle under- or over-dispersed data sets as well. Compared with three other types of models, double Poisson, bivariate Poisson, and zero-inflated double Poisson, the diagonal inflated bivariate Poisson model demonstrates its capability of fitting two data sets with remarkable overlapping portions resulting from the same stochastic process. Therefore, the diagonal inflated bivariate Poisson model provides researchers a new approach to investigating AVCs from a different perspective involving the three distribution parameters (λ(1), λ(2) and λ(3)). The modeling results show the impacts of traffic elements, geometric design and geographic characteristics on the occurrences of both reported AVC and carcass removal data. It is found that the increase of some associated factors, such as speed limit, annual average daily traffic, and shoulder width, will increase the numbers of reported AVCs and carcass removals. Conversely, the presence of some geometric factors, such as rolling and mountainous terrain, will decrease the number of reported AVCs.

Gaussian Mixture Model-Based Speed Estimation and Vehicle Classification Using Single-Loop Measurements

Traffic speed and length-based vehicle classification data are critical inputs for traffic operations, pavement design and maintenance, and transportation planning. However, they cannot be measured directly by single-loop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. In this study, a Gaussian mixture model (GMM)-based approach is developed to estimate more accurate traffic speeds and classified vehicle volumes using single-loop outputs. The estimation procedure consists of multiple iterations of parameter correction and validation. After the GMM is established to empirically model vehicle on-times measured by single-loop detectors, the optimal solution can be initially sought to separate length-based vehicle volume data. Based on the on-time of the separated short vehicles from the GMM, an iterative process will be conducted to improve traffic speed and classified volume estimation until the estimation results become statistically stable and converge. This method is straightforward and computationally efficient. The effectiveness of the proposed approach was examined using data collected from several loop stations on Interstate 90 in the Seattle area. The traffic volume data for three vehicle classes are categorized based on the proposed method. The test results show the proposed GMM approach outperforms the previous models, including conventional constant g-factor method, sequence method, and moving median method, and produces more reliable, accurate estimates of traffic speeds and classified vehicle volumes under various traffic conditions.

Detection and Correction of Inductive Loop Detector Sensitivity Errors by Using Gaussian Mixture Models

Inductive loop detectors (ILDs) form the backbone of many traffic detection networks by providing vehicle detection for freeway and arterial monitoring as well as signal control. Unfortunately, ILD technology generally has limited the available sensitivity settings. Changing roadway conditions and aging equipment can cause ILD settings that had been correct to become under- or oversensitive. ILDs with incorrect sensitivities may result in severe errors in occupancy and volume measurements. Therefore, sensitivity error identification and correction are important for quality data collection from ILDs. In this study, the Gaussian mixture model (GMM) is used to identify ILDs with sensitivity problems. If the sensitivity problem is correctible at the software level, a correction factor is then calculated for the occupancy measurements of the ILD. The correction methodology developed in this study was found effective in correcting occupancy errors caused by the ILD sensitivity problems. Single-loop speed calculation with the corrected occupancy increases the accuracy by 12%. Since this GMM-based approach does not require hardware changes, it is cost-effective and has great potential for easy improvement of archived loop data quality.

Improving intersection performance with left turn phase reservice strategies

Advanced signal operations offer practitioners an opportunity to reduce delay at their intersections at minimal cost by using features already included in their signal control software. One advanced operations technique is phase reservice and its associated feature conditional reservice. This research examines the impact of applying these techniques to the left turn at Hall Blvd. on 99W in Tigard, OR. Significant delay savings were seen for the left turn movement with an average delay reduction of more than 30 seconds at the cost of approximately 3 seconds increased delay for the opposing through movement. Intersection delay reduction was found to be dependent on the ratio of left turning vehicles to opposing through traffic.

A Pitfall to Avoid When Issuing Transit Signal Priority Treatments under Coordinated Control Strategies

Transit Signal Priority (TSP) is an advanced control mechanism to facilitate transit vehicle operations along signalized arterials. In practical application, TSP systems are integrated with coordinated signal control strategies. People take for granted the reduced transit delays through TSP treatments. However, due to conflicts between TSP control schemes and coordinated signal control strategies along signalized arterials, the benefits achieved by transit vehicles may be washed out to some extent. Our recent study for optimizing the South Snohomish Regional Transit Signal Priority (SS-RTSP) system operations found that transit vehicle delays over a signal-coordinated corridor can be lengthened by TSP treatments in some scenarios. A VISSIM-based simulation model is developed to emulate TSP system operations along the SR-99 arterial covering 13 intersections, in the City of Lynnwood, Washington. Various transit operation scenarios under diverse coordinated signal control plans are designed. Theoretical analysis is also provided to formulate the transit travel process. The research findings indicated that to achieve the best operational efficiency, the compatibility between TSP control schemes and signal control coordination should be strengthened to minimize transit disruption to signal coordination.