2013 Session: 796

• Hybrid Approach for Clustering Vehicle Classification Data to Support Regional Implementation of Mechanistic-Empirical Pavement Design Guide
  Abstract: This paper develops a hybrid approach for analyzing vehicle classification data and applies the approach to a fused dataset from multiple jurisdictions in the Canadian Prairie Region. Application of the approach results in a set of regional default truck traffic classification groups (TTCGs) for use in the Mechanistic Empirical Pavement Design Guide (MEPDG). The hybrid approach is a conglomeration of three components: statistical clustering procedures, engineering judgment, and industry intelligence. By applying the hybrid approach, analysts receive the joint benefits of analytical rigor and industry-oriented pragmatism. Application of this approach results in eight TTCGs for the Canadian Prairie Region, which exhibit distinct differences from the default distributions developed for national use in the United States.The benefits of the hybrid approach on fused datasets include: (a) the statistical strength gained from utilizing additional classification data, (b) the development of TTCGs that better reflect the diversity of patterns in the Region, and (c) the potential for improved ability to capture future shifts in truck traffic characteristics based on experience gained in other jurisdictions. The study also identifies limitations to the hybrid approach that should be considered. These limitations include varying data quality between jurisdictions, the sensitivity of low-volume sites to changes in industry patterns and the ability to track these changes, and a shortage of continuous classification sites in the Region. With a clear understanding of its benefits and limitations, the hybrid approach can be applied to truck traffic data analyses in any jurisdiction.
  Authors: Reimer, Mark Jonathon; Regehr, Jonathan D.
  Authors: Reimer, Mark Jonathon; Regehr, Jonathan D.
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology; Operations and Traffic Management
  Session: 796
  Paper Number: 13-2849
• Locating Traffic Sensors on a Highway Network: Models and Algorithms
  Abstract: We consider the problem of finding optimal sensor locations on a traffic network so as to characterize system use overall. We study the problem under two practical scenarios. In the first scenario, we assume there is a given number of sensors (p) that we need to locate on the highway network. In this context, the problem is to find a collection of p locations among a given collection of candidate locations. In the second scenario, we assume that there is a cost (ci) associated with installing a sensor at each candidate location i, and a total budget b. In this context, the problem is to find a collection of locations that provide the best possible characterization given the budget constraint. We propose a metric to evaluate a potential solution and then propose appropriate mathematical models for solving the problem for each scenario. We show that the budget-constrained problem is an extension of the well-known p-median problem. A new Lagrangian heuristic algorithm is presented to solve large instances of this problem where a budget constraint is imposed. Through a comprehensive computational experiment, we demonstrate that the Lagrangian heuristic algorithm provides solutions for large-scale networks within reasonable execution times. Examples are based on locating weigh-in-motion (WIM) sensors on a large-scale highway network.
  Authors: Sayyady, Fatemeh; Fathi, Yahya; List, George F.; Stone, John R.
  Authors: Sayyady, Fatemeh; Fathi, Yahya; List, George F.; Stone, John R.
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology; Operations and Traffic Management
  Session: 796
  Paper Number: 13-2853
  Practice-Ready: Yes
• Evaluation of Reduced Traffic Data Collection Plans using Reduction-Effectiveness Ratios
  Abstract: Because of budget shortfalls in recent years, state departments of transportation (DOTs) need to adjust their traffic data collection plans by reducing data collection locations and/or extending data collection cycles; however, only limited studies have been performed to evaluate the cost effectiveness of different data collection reduction efforts. The purpose of this study is to develop a quantitative method for evaluating the impact of different reduced traffic data collection plans on the overall accuracy of the annual average daily traffic (AADT) estimation. To compare the accuracy of ten reduced data collection plans with a base plan, the mean absolute percentage error (MAPE) is calculated. In addition, the reduction effectiveness ratio (i.e., the percent of reduced data collection cost to the percent of increased AADT estimation error) is proposed in this study. Results of this study show that while the current practice, which randomly selects data collection sites based on different cycles, performs well in maintaining AADT estimation accuracy, it may not be the most cost-effective approach. Results also show that certain types of sites, e.g., rural sites, lower AADT sites, and higher AADT variation sites, tend to produce larger errors if they are not counted. These results imply that the proposed method provides a quantitative means to evaluate different reduced data collection plans. It enlightens directions to enhance current data collection and traffic estimation practices. More importantly, it enriches the information provided for state DOTs to make informed, and effective, decisions under the stringent budget.
  Authors: Wang, Chieh (Ross); Tsai, Yichang (James)
  Authors: Wang, Chieh (Ross); Tsai, Yichang (James)
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology; Operations and Traffic Management
  Session: 796
  Paper Number: 13-2183
  Practice-Ready: Yes
• Evaluation of Reduced Traffic Data Collection Plans using Reduction-Effectiveness Ratios
  Authors: Wang, Chieh Ross
  Authors: Wang, Chieh Ross
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Operations and Traffic Management
  Session: 796
  Paper Number: 13-2183
• Locating Traffic Sensors on a Highway Network: Models and Algorithms
  Authors: Sayyady, Fatemeh
  Authors: Sayyady, Fatemeh
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Operations and Traffic Management
  Session: 796
  Paper Number: 13-2853
• Hybrid Approach for Clustering Vehicle Classification Data to Support Regional Implementation of Mechanistic-Empirical Pavement Design Guide
  Authors: Reimer, Mark
  Authors: Reimer, Mark
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Operations and Traffic Management
  Session: 796
  Paper Number: 13-2849
• New Traffic Monitoring Guidance for Data-Driven Decision Support
  Authors: Vandervalk-Ostrander, Anita
  Authors: Vandervalk-Ostrander, Anita
  Year: 2013
  Document Type: Presentation
  Subject: Data and Information Technology; Operations and Traffic Management
  Session: 796
  Paper Number: P13-6720