DEVELOPMENT OF A STATISTICALLY-BASED METHODOLOGY FOR ANALYZING AUTOMATIC SAFETY TREATMENTS AT ISOLATED HIGH-SPEED SIGNALIZED INTERSECTIONS

Crashes at isolated rural intersections, particularly those involving vehicles traveling perpendicularly to each other, are especially dangerous due to the high speeds involved. Consequently, transportation agencies are interested in reducing the occurrence of this crash type. Many engineering treatments exist to improve safety at isolated, high-speed, signalized intersections. Intuitively, it is critical to know which safety treatments are the most effective for a given set of selection criteria at a particular intersection. Without a well-defined decision making methodology, it is difficult to decide which safety countermeasure, or set of countermeasures, is the best option. Additionally, because of the large number of possible intersection configurations, traffic volumes, and vehicle types, it would be impossible to develop a set of guidelines that could be applied to all signalized intersections. Therefore, a methodology was developed in in this paper whereby common countermeasures could be modeled and analyzed prior to being implemented in the field. Due to the dynamic and stochastic nature of the problem, the choice was made to employ microsimulation tools, such as VISSIM, to analyze the studied countermeasures. A calibrated and validated microsimulation model of a signalized intersection was used to model two common safety countermeasures. The methodology was demonstrated on a test site located just outside of Lincoln, Nebraska. The model was calibrated to the distribution of observed speeds collected at the test site. It was concluded that the methodology could be used for the preliminary analysis of safety treatments based on select safety and operational measures of effectiveness

Driving Performances Assessment Based on Speed Variation Using Dedicated Route Truck GPS Data

It was hypothesized that a driver is not safe when travel speed is too high and also not necessarily safe when travel speed is too low. Based on this hypothesis, this paper studied the risky driving performances by measuring speed variations of a driver’s recurrent trips in two perspectives: 1) driver profiles, which scored the risk on-road driving of each driver and 2) driving patterns, which reflected the risk speed patterns of a type of drivers. The proposed method was tested on a 30-day global positioning system (GPS) dataset, collected from 100 trucks. The study first split the raw dataset into trips and finds the most repeatedly traveled route. Next, the frequency and amplitude of the speed variations from trips of each truck are calculated to establish driver profiles. A risk score is used to rank the truck drivers, i.e., a higher score indicates that the truck driver is more likely to conduct risky driving performances. All trucks are featured in four pre-defined driving patterns according to the different types of speed variations. The geospatial speed distribution of several trucks is manually examined from the raw dataset to verify the results. The contribution lies in providing a method to evaluate a driver’s risk performance through mass truck GPS data. The proposed method would help for monitoring on-road risky driving performances in large fleet management and also providing knowledge about driving styles among drivers which would be beneficial in study driver assistant system

Safety Effectiveness of Offsetting Opposing Left-Turn Lanes: A Case Study

This paper discusses the benefits to intersection safety of offsetting left-turn lanes by widening the width of the lane-line marking between the left-turn lanes and their adjacent through lanes. The analysis was performed using an empirical Baye's procedure in order to account for potential bias due to regression-to-the-mean. Results from the analysis of 12 treated intersection approaches and 36 non-treated approaches in Lincoln, Nebraska, suggest statistically significant improvements in safety at the treated intersections

Assessing Passenger Car Equivalency Factors for High Truck Percentages

The Passenger Car Equivalent (PCE) values in the Highway Capacity Manual (HCM) 2010 might not be valid for western Nebraska freeway conditions. This is because: 1) Interstate 80 (I-80) experiences high truck percentages (25% to 60%), while the HCM provides PCE values up to 25% of the truck percentage; 2) the average speed of trucks are observed lower than passenger cars, which is incompatible with the HCM assumption that the free speed of all vehicle types is the same at level terrain; and 3) it is unclear whether the \u201caverage\u201d truck used in the simulation study for PCE values in the HCM is representative of a typical Nebraska truck. Also, a platoon may form when a truck passes another, resulting in a delay for vehicles that are following, who may wish to be traveling at a faster speed. The objective of this research is to estimate and recommend PCEs for basic freeway segments on I-80 with a high truck percentage in western Nebraska. This research study will examine aspects of the current HCM PCE determination methodology to see if it is representative of Nebraska\u2019s traffic on basic freeway segments. To accomplish these tasks, field data was collected using ITS data collection equipment, including video and radar detectors. This data will be used to: 1) analyze characteristics of platoons on I-80; 2) calibrate a VISSIM 5.4 traffic simulation model that can be used to estimate PCE values in a manner similar to that used to calculate the HCM values; and 3) calculate PCEs using a variety of approaches (e.g., headway-based method and delay-based method). The PCE values under truck restriction conditions are also calculated using these simulation data. The results suggest the PCEs in the HCM 2010 for level freeway segments (1.5) may not be suitable for traffic flow on I-80 in western Nebraska. The PCEs based on the equal-density method (HCM method) using the different speed distributions for trucks and passenger cars with an average of 3.0, and the PCEs based on the delay method with an average of 2.8, are finally recommended

Prediction Model of Bus Arrival Time for Real-Time Applications

Advanced traveler information systems (ATIS) are one component of intelligent transportation systems (ITS), and a major component of ATIS is travel time information. Automatic vehicle location (AVL) systems, which are a part of ITS, have been adopted by many transit agencies to track their vehicles and to predict travel time in real time. Because of the complexity involved, there is no universally adopted approach for this latter application, and research is needed in this area. The objectives of the research in this paper are to develop a model to predict bus arrival time using AVL data and apply the model for real-time applications. The test bed was a bus route located in Houston, Texas, and the travel time prediction model considered schedule adherence, traffic congestion, and dwell times. A historical data-based model, regression models, and artificial neural network (ANN) models were used to predict bus arrival time. It was found that ANN models outperformed both the historical data-based model and the regression model in terms of prediction accuracy. It was also found that the ANN models can be used for real-time applications

Travel time prediction by advanced neural network

The Advanced Traffic Management System of San Antonio, Texas, called TransGuide System uses a sensor system installed in 26 miles of highway to feed data to a high speed computer network for analysis. The portions of interstates involved were generally confined to central city areas and did not reach the first outer loop that surrounds the inner city. The objective of this paper is to build a real-time travel time prediction model for the freeway network of San Antonio based on the information collected by the loop sensor and GPS systems. The travel time prediction of the model could be the basis of later traffic management systems and also used by the traveler information systems. The robustness and accuracy of the model is a very important feature because traffic management systems depend on driver acceptance and compliance to be effective. This paper examines first the use of Modular Neural Networks (MNN) to forecast multipleperiods of traffic engineering features, such as speed, occupancy and volume, and then determines the expected travel times based on these predicted values, using currently applied methods. Secondly, the multiple-periods travel times are predicted directly from the loop data with an MNN. The models are tested and trained on actual travel times from San Antonio, collected by GPS data system. Then the results of the two models are compared to each other and to the results of standard travel time prediction models

FORECASTING TRAIN TRAVEL TIMES AT AT-GRADE CROSSINGS

The ability to accurately forecast train arrival times is essential for the safe and efficient operation of highway-railroad grade crossings (HRGCs). Trains in the United States are required to give a minimum of 20 s of warning time before arriving at an HRGC. With the recent development of new detection-equipment technology, detectors potentially could be employed further upstream of the HRGC, which would result in earlier detection times. This information would be particularly useful for preemption strategies at signalized intersections located near the HRGC (IHRGCs). For example, earlier warning times could be used to reduce or eliminate the risk of unsafe pedestrian movements at IHRGCs. In this study, a modular artificial neural network (ANN) was used to forecast the train arrival time at an HRGC. An ANN was adopted because there is a nonlinear relationship between the independent variables such as train speed profile and the dependent variable arrival time at an HRGC. A modular approach was used because the trains often have different characteristics depending on their cargo and the operational rules in effect at the time they are detected. Because the train detection is continuous, different models were developed for each separate data input. In this case, the prediction interval update was assumed to be 10 s and 24 models were developed. Approximately 499 trains were used for training the ANN and 183 trains were used for testing. It was found that a modular architecture gave superior results to that of a simple ANN model, standard regression techniques, and current forecasting methods for the entire detection time period. It was found that, with an increase in detection time, the forecast accuracy increases for all methods and the prediction interval tends to decrease

Development of a statistically-based methodology for analyzing automatic safety treatments at isolated high-speed signalized intersections

Crashes at isolated rural intersections, particularly those involving vehicles traveling perpendicularly to each other, are especially dangerous due to the high speeds involved. Consequently, transportation agencies are interested in reducing the occurrence of this crash type. Many engineering treatments exist to improve safety at isolated, high-speed, signalized intersections. Intuitively, it is critical to know which safety treatments are the most effective for a given set of selection criteria at a particular intersection. Without a well-defined decision making methodology, it is difficult to decide which safety countermeasure, or set of countermeasures, is the best option. Additionally, because of the large number of possible intersection configurations, traffic volumes, and vehicle types, it would be impossible to develop a set of guidelines that could be applied to all signalized intersections. Therefore, a methodology was developed in in this paper whereby common countermeasures could be modeled and analyzed prior to being implemented in the field. Due to the dynamic and stochastic nature of the problem, the choice was made to employ microsimulation tools, such as VISSIM, to analyze the studied countermeasures. A calibrated and validated microsimulation model of a signalized intersection was used to model two common safety countermeasures. The methodology was demonstrated on a test site located just outside of Lincoln, Nebraska. The model was calibrated to the distribution of observed speeds collected at the test site. It was concluded that the methodology could be used for the preliminary analysis of safety treatments based on select safety and operational measures of effectiveness

Performance of Advance Warning Systems in a Coordinated System

The Advance Warning System (AWS), developed by the Nebraska Department of Roads (NDOR) has proven to be effective at improving traffic safety at isolated signalized intersections. However, the effectiveness of the system has not been analyzed at signalized intersections operating in a coordinated mode. This project analyzed AWS on arterials where the signals operate in a coordinated mode. The test bed consisted of nine sites, which are located at five successive coordinated signalized intersections on Highway 281 in Grand Island, Nebraska. A non-intrusive data collection system was used to collect a continuous traffic stream of data up to 1200 ft upstream of the stop-line at a given intersection. The analysis showed that with the AWS, the dilemma zone entrapment rate was, on average, 81% smaller than what would have been expected if the AWS was not installed. The accelerating/decelerating analysis showed that 94% of the average acceleration/deceleration rates were within the comfortable range, 69.7% of the vehicles slowed down after the start of the AWS signal, and 92.1% of vehicles slowed down after the start of amber. The red-light running analysis showed that the percentage of red-light running occurrence ranged from 0.9% to 2.0%, with an average of 1.5% and a standard deviation of 0.4%. These results indicated that most of the vehicles were in compliance. The simulation-based conflict analysis showed that, on average, there was a 55%, 12%, and 51% reduction in rear-end, lane-change, and crossing conflicts, respectively, for all nine sites, when the AWS system was applied. The overall results suggested that the AWS was effective at alerting drivers to the impending end of the green signal, which resulted in a reduction of conflicts and a safer corridor