Quantitative Multi-Criteria Decision Support Approach for Prioritizing Grade Crossings to Address the Problem of Blocked Crossings

How should an agency proactively identify and prioritize rail-highway grade crossings for intervention in relation to their likely performance benefits for the community and the system? Performance is mainly related to two factors: (i) the likelihood and duration of “blockage,” that is, the temporary obstruction of a road link when a train blocks the intersecting grade crossing, and (ii) the “hazard condition” of the crossing itself, with regard to its design and asset quality. Existing analytical grade crossing prioritization models focus almost solely on safety and crash prevention by addressing the “hazard condition” aspect of performance. The paper develops a quantitative multi-criteria approach that not only considers and builds on existing analytical safety-based approaches, but goes beyond and also includes considerations such as blocked crossings. The model calculates a multi-dimensional adaptive capacity score (ACS) for each crossing, which can then be used to rank a set of crossings, as well as test different policy scenarios and their effect on prioritization. The model is applied to approximately 5,700 grade crossings in the State of Ohio and detailed pilot validations with stakeholders in two counties conducted. Based on the validation process, the model provides comprehensive and cohesive results and is being deployed statewide. Next steps involve incorporating considerations for truck traffic and pedestrians/bicycles (non-motorized users) in calculating the ACS as well as developing mitigation measures for blocked crossings that can be considered from the model.

A Holistic Analysis of Train-Vehicle Accidents at Highway-Rail Grade Crossings in Florida

Highway-rail grade crossing (HRGC) accidents pose a serious risk of safety to highway users, including pedestrians trying to cross HRGCs. A significant increase in the number of HRGC accidents globally calls for greater research efforts, which are not limited to the analysis of accidents at HRGCs but also understanding user perception, driver behavior, potential conflicting areas at crossings, effectiveness of countermeasures and user perception towards them. HRGC safety is one of the priority areas in the State of Florida, since the state HRGCs experienced a total of 429 injuries and 146 fatalities between 2010 and 2019 with a significant increase in HRGC accidents over the last years. The present study aims to conduct a comprehensive analysis of the HRGCs that experienced accidents in Florida over the last years. The databases maintained by the Federal Rail Administration (FRA) are used to gather the relevant information for a total of 578 crossings that experienced at least one accident from 2010 to 2019. In contrast with many of the previous efforts, this study investigates a wide range of various factors, including physical and operational characteristics of crossings, vehicle and train characteristics, spatial characteristics, temporal and environmental characteristics, driver actions and related characteristics, and other relevant information. The outcomes of this research will help better understanding the major causes behind accidents at the HRGCs in the State of Florida in a holistic way by considering a variety of relevant factors, which will assist the appropriate stakeholders with implementation of safety improvement projects across the state.

Rail noise grade separation alternative analysis case study

The San Francisco Bay Area has an existing commuter rail system that brings commuters from southern regional communities into the downtown city center. One of the communities served by commuter rail service is the City of Palo Alto, CA, which includes four active grade crossings, each requiring train horn sounding for each train event. The City wished to evaluate various options to eliminate the noise generated from horn soundings by creating road/rail grade separations at each existing grade crossing and other possible noise and vibration control elements. The alternatives included crossing closures, rail bed trenching, viaducts, roadway underpasses, and tunnels. A noise and vibration study was undertaken to provide an analysis of which alternatives would provide better reductions in noise and vibration in the surrounding community. The study included an assessment of existing noise levels and predicted future noise and vibration levels for construction and operation of each proposed alternative using current established noise and vibration methodology. The results of this study included comparisons of the noise and vibration associated with each of the of the proposed alternatives that could be used in conjunction with other studies considering cost, traffic, safety, aesthetics and other factors to select an overall preferred alternative.

A case study on the noise effects of elevating existing train tracks

When modeling rail noise on an elevated track, there are several adjustments that need to be considered relative to modeling at-grade operations. These adjustments include the effects of re-radiated noise from the track and support structure, reduced ground attenuation due to an elevated noise source and a reduction in the potential for shielding from adjacent rows of buildings. These adjustments are built into the model as a part of the design of a project. This case study examines a unique situation where a project involved elevating existing at-grade tracks to eliminate a bottleneck related to an at-grade crossing of two perpendicular train tracks. The project elevated one main track over the other and shifted the track closer to noise sensitive receivers. The US Federal Transit Administration and Federal Railroad Administration guidance, which were used to assess noise impacts, produced unexpected results during the initial assessment due mainly to the assumptions regarding the changes in shielding and ground attenuation with the elevated structure. This presentation will discuss the initial assumptions used in the project, the limitations of the model relative to changes in shielding and ground attenuation, and the solutions that were implemented to obtain reasonable results for the impact assessment.

A Resource Optimization Framework for Improving Railway-Highway Grade Crossing Safety in Canada

This paper presents a new approach to address the problem of allocating federal resources and identifying upgrading projects for improving the safety of at-grade crossings in Canada. The proposed approach is unique in two key aspects. First, a risk-based network screening process is adopted to identify the priority sites for providing a justifiable basis for distributing the total budget at a regional level as well as narrowing the search space in the subject optimization step. Secondly, a mathematical programming approach is applied to formalize the resource allocation process with explicit consideration of the expected benefits - risk reduction and the costs of implementing the projects. This approach is expected to improve the process of identifying the optimal set of upgrading projects within each region, thus maximizing the return of investment. A full-scale case study from the Canadian crossing network is conducted to demonstrate the application of the proposed approach.

A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings: The Competing Risks Method

This paper proposes a mathematical model, the competing risks method, to investigate highway-rail grade crossing (HRGC) crash frequency and crash severity simultaneously over a 30-year period. The proposed competing risks model is a special type of survival analysis to accommodate the competing nature of multiple outcomes from the same event of interest; in this case, the competing multiple outcomes are crash severities, while event of interest is crash occurrence. Knowledge-gain-based benefits to be discovered through the application of this model and 30-year dataset are as follows: (1) a straightforward and integrated one-step estimation process that considers both crash frequency and severity likelihood in the same model, so direct hazard ranking considering both crash frequency and severity likelihood is possible; (2) interpretative effects of identified covariates from both the direction and magnitude perspectives; and (3) the long-term cumulative effect of contributors with the cumulative incidence function.

A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis

Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past. Data-mining models focus on prediction while dominant general linear models focus on model and data fitness. Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement. The gradient boosting (GB) model has gained popularity in many research areas. In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model with functional gradient descent algorithm is selected to analyze crashes at highway-rail grade crossings (HRGCs) and to identify contributor factors. Moreover, contributors’ importance and partial-dependent relations are generated to further understand the relationship of identified contributors and HRGC crash likelihood to concur “black box” issues that most machine learning methods face. Furthermore, to fully demonstrate the model’s prediction performance, a comprehensive model prediction power assessment based on six measurements is conducted, and the prediction performance of the GB model is verified and compared with a decision tree model as a reference due to their popularity and comparable data availability. It is demonstrated that the GB model produces better prediction accuracy and reveals nonlinear relationships among contributors and crash likelihood. In general, HRGC crash likelihood is significantly impacted by several traffic exposure factors: highway traffic volume, railway traffic volume, and train travel speed and others.