Roemer M. Alfelor

Calibration of traffic flow models under adverse weather and application in mesoscopic network simulation

The weather-sensitive traffic estimation and prediction system (TrEPS) aims for accurate estimation and prediction of the traffic states under inclement weather conditions. Successful application of weather-sensitive TrEPS requires detailed calibration of weather effects on the traffic flow model. In this study, systematic procedures for the entire calibration process were developed, from data collection through model parameter estimation to model validation. After the development of the procedures, a dual-regime modified Greenshields model and weather adjustment factors were calibrated for four metropolitan areas across the United States (Irvine, California; Chicago, Illinois; Salt Lake City, Utah; and Baltimore, Maryland) by using freeway loop detector traffic data and weather data from automated surface-observing systems stations. Observations showed that visibility and precipitation (rain–snow) intensity have significant impacts on the value of some parameters of the traffic flow models, such as free-flow speed and maximum flow rate, while these impacts can be included in weather adjustment factors. The calibrated models were used as input in a weather-integrated simulation system for dynamic traffic assignment. The results show that the calibrated models are capable of capturing the weather effects on traffic flow more realistically than TrEPS without weather integration.

Implementation and Evaluation of Weather-Responsive Traffic Management Strategies

This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies.

TRACK DEGRADATION ASSESSMENT USING GAGE RESTRAINT MEASUREMENTS

Gage restraint is an important indicator of track condition and safety. In 1999, approximately 13 percent of derailments were caused by reductions in gage restraint and the resulting widening of the track gage. Existing techniques for the measurement of gage restraint allow identification of track sections with weak lateral support. However, little has been done to investigate the change in, or weakening of, gage restraint over time as a function of track, traffic, and environmental parameters. A track degradation assessment study is under way to develop models that can be used to predict changes in gage restraint by using data obtained from the automated Gage Restraint Measurement System. The degradation models will be useful for forecasting the future condition of the track, determining the appropriate frequency and timing of track inspections, and evaluating the effectiveness of maintenance strategies. A literature review of track degradation models and previous work on gage restraint analysis is presented. The rationale for adoption of an empirical approach to gage restraint degradation modeling is explained. The processing applied to the automatically collected data and the preliminary database program developed to store the information and estimate track degradation equations are also described. The track degradation analysis and database development study currently focuses on gage restraints and track geometry parameters as measures of condition. In the future, this can be extended to include other degradation parameters for a comprehensive track performance analysis.

Impact of Inclement Weather on Left-Turn Gap Acceptance Behavior of Drivers

This paper describes an empirical study conducted to quantify the impact of a number of variables on left-turn gap acceptance behavior of drivers at signalized intersections. The variables include the gap duration, the travel time needed to cross the intersection, and the corresponding weather condition. The gap acceptance data set used in the study included 11,114 observations (1,176 accepted gaps and 9,938 rejected gaps) for a permitted left-turn maneuver at a signalized intersection; the data were gathered over 6 months. The data set was divided into six weather categories for different combinations of precipitation and roadway surface conditions. Logistic regression models were calibrated to the data and compared to identify the best model for capturing gap acceptance behavior of drivers. The models reveal that drivers are more conservative during snow than rain. Drivers require larger gaps for wet surface conditions than for snowy and icy surface conditions, and drivers require the smallest gaps for dry roadway conditions. In addition, the models show that drivers require larger gaps as the distance required to traverse the offered gap increases. The study also shows how inclement weather and the number of opposing lanes affect permitted left-turn saturation flow rates. It is anticipated that these findings will be used to develop weather-specific traffic signal timings that account for changes in traffic stream saturation flow rates and also used for intelligent assistance systems for drivers.

Empirical Study of Impact of Icy Roadway Surface Condition on Driver Car-Following Behavior

Research was done to quantify the impact of icy roadway conditions on driver car-following behavior. The data used in the study were gathered by a group of researchers at Japans Hokkaido University in a controlled environment under dry and icy roadway conditions. The data were used to calibrate the Van Aerde steady-state car-following model, along with vehicle acceleration and deceleration constraints. The impact of icy roadway conditions on five driver-specific car-following parameters [driver perception–reaction time (PRT), free-flow or desired speed, speed-at-capacity, capacity, and jam density] was conducted with one-way analysis of variance and Kruskal–Wallis tests. The results demonstrate that icy roadway conditions produce statistically significant differences at a 5% significance level. Specifically, icy roadway conditions reduce the mean free-flow speed, speed at capacity, and capacity by 28%, 13%, and 46%, respectively, compared with driving on dry roadways. The mean PRT for icy conditions is found to take 13% longer than driving under dry conditions. The longer PRTs can be attributed to drivers driving at lower speeds and larger spacing on icy surfaces compared with dry conditions.

Customer-oriented maintenance decision support system : Developing a prototype

Performance of highway maintenance units has been traditionally measured by output or production rates. Today, more and more highway agencies are incorporating customer-oriented performance measures to evaluate their maintenance management processes. The Minnesota Department of Transportation (MnDOT) has been adapting and applying private-sector business practices to the management of its maintenance functions. The department initiated a maintenance business planning (MBP) project several years ago to focus on rethinking highway maintenance from the customer’s perspective. A prototype decision support system (DSS) was developed that builds on the MBP by modeling performance measurement parameters and implementing analytical processes that can support MnDOT’s maintenance managers in responding to customer needs. The DSS uses input, output, outcome, environment, and customer data from existing databases to measure efficiency and effectiveness of maintenance activities, as defined by the customer. The pilot system focuses on two of seven groups of maintenance products and services—clear roads and attractive roadsides. The prototype DSS displays data in formats useful for evaluating maintenance work processes, benchmarking work units’ performance, evaluating efficiency and effectiveness in meeting customer needs, and deploying resources. Models that calculate the value added to customers in terms of road-user costs (i.e., travel time and accident costs) and customer willingness to pay for attractive roadside products were developed and incorporated in the DSS. If fully implemented, the DSS can be a powerful tool for maintenance decision making that can be applied to other state maintenance agencies.

Online implementation and evaluation of weather-responsive coordinated signal timing operations

This paper presents the development and application of weather-responsive traffic management strategies and tools to support coordinated signal timing operations with traffic estimation and prediction system (TREPS) models. First, a systematic framework for implementing and evaluating traffic signal operations under severe weather conditions was developed, and activities for planning, preparing, and deploying signal operations were identified in real-time traffic management center (TMC) operations. Next, weather-responsive coordinated signal plans were designed and evaluated with the TREPS method and a locally calibrated network. Online implementation and evaluation was conducted in Salt Lake City, Utah—the first documented online application of TREPS to support coordinated signal operation in inclement weather. The analysis results confirm that the deployed TREPS, which is based on DYNASMART-X, is able to help TMC operators test appropriate signal timing plans proactively under different weather forecasts before deployment and is capable of using real-time measurements to improve the quality and accuracy of the systems estimations and future predictions through detectors and roadside sensor coverage.

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

Development of Real-time Simulation-based Decision Support System for Weather Responsive Traffic Signal Operations

This paper presents a framework and methodology for integrating real-time Traffic Estimation and Prediction Systems (TrEPS) into weather-responsive traffic signal operations in Utah, USA. This study is motivated by the need for adjusting signal timing plans in response to changing traffic conditions during inclement weather in order to mitigate the impact of weather and maintain the network service level. This study provides a real-world application demonstrating that such a need can be effectively assisted by a TrEPS-based decision support system. Three components are introduced to form the overall decision support system: real-time TrEPS, Scenario Manager, and Scenario Library. Real-time TrEPS offers the capability to estimate and predict network states under various control scenarios; Scenario Manager provides an environment to identify and evaluate alternative signal control strategies based on TrEPS-predicted network states; and Scenario Library serves as a knowledge base defining available weather-responsive signal timing plans for Scenario Manager to access in real-time. The detailed implementation procedure and expected benefits of the proposed system are illustrated and discussed using a case study based on a historical snow event.