Optimizing work zones for highway maintenance with floating car data (FCD) : final report.

Chien, Steven; Mouskos, Kyriacos C. · 2015 · ROSA P / University Transportation Research Center

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Summary

This study addresses the inefficiencies in current Department of Transportation (DOT) models for managing highway maintenance work zones. Existing analytical models rely on traditional volume/capacity formulas and deterministic queuing theory, which often yield inaccurate estimates of traffic delay, speed, and associated costs because they fail to account for temporal and spatial traffic variations. The research aims to develop a methodology that utilizes Floating Car Data (FCD)—derived from Global Navigation Satellite Systems (GNSS) and Bluetooth technology—to estimate traffic flow characteristics more accurately. The primary objective is to minimize the total work zone impact cost, defined as the sum of maintenance costs, idling costs, vehicle emissions, and user costs, by optimizing work zone lengths and schedules. The researchers formulated the problem as a combinatorial optimization task subject to practical constraints, including total project length, minimum duration of maintenance activities, and maximum project duration. To solve this, they developed a heuristic method using Genetic Algorithms (GA) implemented via MATLAB’s Global Optimization Toolbox. The GA was designed with a specific data structure to reduce variable counts and employed penalty functions to handle constraints efficiently. The model integrates real-time traffic flow and speed information from FCD sources, such as GNSS-enabled vehicles and Bluetooth roadside readers, to provide precise estimates of travel time and delay. The methodology was validated through two case studies on a segment of Interstate I-287 in New Jersey. In the first case study, involving a 1.8-mile segment, the model predicted a 9-hour completion time by deploying a single work zone during nighttime hours. The second case study, covering a 5-mile segment, resulted in an optimal schedule comprising three periods: two night shifts and one off-peak period. Sensitivity analyses were conducted to evaluate the impact of varying traffic volumes, production rates, and project duration constraints on the total minimized cost. The results demonstrated that the GA-based approach could effectively balance agency maintenance costs with user delay and environmental costs. The significance of this work lies in its ability to provide DOTs with a more accurate tool for work zone management, leveraging the widespread availability of FCD technologies. By incorporating real-time or high-resolution traffic data, the model offers superior estimates of traffic impacts compared to traditional deterministic models. The authors conclude that the methodology can be extended to finer time intervals (e.g., 15 minutes) and adapted for real-time, rolling-horizon execution if continuous traffic data is available. This approach supports the broader goal of reducing congestion costs, which amounted to $121 billion in the US in 2011, and mitigating greenhouse gas emissions from the transportation sector.

Key finding

The Genetic Algorithm-based optimization of work zone schedules using Floating Car Data minimized total work zone costs by determining optimal lengths and timing for maintenance activities on Interstate I-287.

Methodology

modeling

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• work zones