Signal Timing Optimization for Corridors with Multiple Highway-Rail Grade Crossings Using Genetic Algorithm

Safety and efficiency are two critical issues at highway-rail grade crossings (HRGCs) and their nearby intersections. Standard traffic signal optimization programs are not designed to work on roadway networks that contain multiple HRGCs, because their underlying assumption is that the roadway traffic is in a steady-state.During a train event, steady-state conditions do not occur.This is particularly true for corridors that experience high train traffic (e.g., over 2 trains per hour). In this situation, the non-steadystate conditions predominate. This paper develops a simulation-based methodology for optimizing traffic signal timing plan on corridors of this kind.The primary goal is to maximize safety, and the secondary goal is to minimize delay. A Genetic Algorithm (GA) was used as the optimization approach in the proposed methodology. A new transition preemption strategy for dual tracks (TPS DT) and a train arrival prediction model were integrated in the proposed methodology. An urban road network withmultiple HRGCs in Lincoln, NE, was used as the study network.The microsimulation model VISSIMwas used for evaluation purposes and was calibrated to local traffic conditions. A sensitivity analysis with different train traffic scenarios was conducted. It was concluded that the methodology can significantly improve both the safety and efficiency of traffic corridors with HRGCs

An analysis of four methodologies for estimating highway capacity from ITS data

With the recent advent of Intelligent Transportation Systems (ITS), and their associated data collection and archiving capabilities, there is now a rich data source for transportation professionals to develop capacity values for their own jurisdictions. Unfortunately, there is no consensus on the best approach for estimating capacity from ITS data. The motivation of this paper is to compare and contrast four of the most popular capacity estimation techniques in terms of (1) data requirements, (2) modeling effort required, (3) estimated parameter values, (4) theoretical background, and (5) statistical differences across time and over geographically dispersed locations. Specifically, the first method is the maximum observed value, the second is a standard fundamental diagram curve fitting approach using the popular Van Aerde model, the third method uses the breakdown identification approach, and the fourth method is the survival probability based on product limit method. These four approaches were tested on two test beds: one is located in San Diego, California,U.S., and has data from 112 work days; the other is located in Shanghai, China, and consists of 81 work days. It was found that, irrespective of the estimation methodology and the definition of capacity, the estimated capacity can vary considerably over time. The second finding was that, as expected, the different approaches yielded different capacity results. These estimated capacities varied by as much as 26 % at the San Diego test site and by 34 %at the Shanghai test site. It was also found that each of the methodologies has advantages and disadvantages, and the best method will be the function of the available data, the application, and the goals of the modeler. Consequently, it is critical for users of automatic capacity estimation techniques, which utilize ITS data, to understand the underlying assumptions of each of the different approaches

Calibrating the Robertson’s Platoon Dispersion Model on a Coordinated Corridor with Advance Warning Flashers

Platoon dispersion (PD) is the foundation of traffic signal coordination in an urban traffic network. PD describes the phenomenon by which vehicles depart from an upstream intersection as a platoon and begin to disperse before they arrive at the downstream intersection. Recently, advance warning flashers (AWFs) have been applied in many high-speed corridors. There is a need to update the traditional PD model to include the effect of AWFs. This paper examines the traffic flow dispersion patterns when an AWF is present and tests the hypothesis that the AWF will affect PD on a coordinated signal corridor. Platoon vehicles, which are not affected by the operation of the AWF, are used for comparison. Results show that when the AWF effect is included in the PD model, the smoothing factor F of the Robertson’s PD model ranges from 0.11 to 0.13. This range is smaller than the smoothing factor without the AWF effect. The platoon arrival time coefficient a ranges from 0.777 to 0.819 with the AWF effect. This is approximately the same as the default value of 0.8 in the TRANSYT simulation model. The PD coefficient β increases from an average of 0.11 with the AWF effect to an average of 0.24 without the AWF effect, which indicates an increase in roadway friction. It was concluded that AWFs increase the dispersion of the platoons, which might affect signal coordination

Calibrating the Highway Capacity Manual Arterial Travel Time Reliability Model

The latest edition of the Highway Capacity Manual (HCM-6) includes, for the first time, a methodology for estimating and predicting the average travel time distribution (TTD) of urban streets. Travel time reliability (TTR) metrics can then be estimated from the TTD. The HCM-6 explicitly considers five key sources of travel time variability. A literature search showed no evidence that the HCM-6 TTR model has ever been calibrated with empirical travel time data. More importantly, previous research showed that the HCM-6 underestimated the empirical TTD variability by 70% on a testbed in Lincoln, Nebraska. In other words, the HCM-6 TTR metrics reflected a more reliable roadway than would be supported by field measurements. This paper proposes a methodology for calibrating the HCM-6 TTR model so that it better estimates the empirical TTD. This calibration approach was used on an arterial roadway in Lincoln, Nebraska, and no statistically significant differences were found between the calibrated HCM-6 TTD and the empirical TTD at the 5% significance level

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

Studying Platoon Dispersion Characteristics Under Heterogeneous Traffic in India

AbstractPlatoons are created by signalized intersections which control travel patterns of vehicles. It is common for platoons to disperse during normal traffic operations. While platoon dispersion has been studied extensively under homogenous and lane disciplined traffic conditions, this characteristic under heterogeneous conditions has not been addressed adequately. This study will be one of the first steps in this direction and will lead to better understanding and simulation of platoon dispersion under Indian conditions.Data describing platoon dispersion was collected with video recording systems along an arterial in Chennai, India. The Robertson's model parameters calibrated for this data was found to be very different from those obtained in previous studies by other researchers. This could be attributed to the highly heterogeneous traffic existing on Indian roads. Dispersion characteristics were studied by taking each platoon individually. The study will augment the understanding of platoon dispersion by discussing the issue of heterogeneous conditions