Modeling and Control of HOT Lanes - Phase II

Kurzhanskiy, Alex A. · 2019 · ROSA P / California Department of Transportation. Division of Rail and Mass Transportation

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Summary

This report presents the development of a quantitative assessment toolbox for modeling and controlling High-Occupancy Toll (HOT) lanes, specifically addressing the operational challenges caused by policy violations. The research was motivated by observations that HOT lanes, such as those on the I-10 West freeway in Los Angeles, frequently fail to maintain target speeds (e.g., 45 mph) due to congestion. A significant factor in this congestion is the misrepresentation of vehicle occupancy by drivers; manual counts revealed that a large portion of vehicles registered as high-occupancy vehicles (3+HOV) were actually single-occupancy vehicles (SOVs) evading tolls. The project aimed to create a multimodal macroscopic simulation model capable of capturing these violator behaviors to evaluate operational scenarios and enforcement strategies. The methodology employs a multimodal macroscopic traffic model that distinguishes between four vehicle commodities: low-occupancy vehicles (LOVs) unwilling to use the HOT lane, high-occupancy vehicles (HOVs), LOVs willing to pay the toll, and LOVs willing to violate policy. The model incorporates a HOT lane controller that dynamically adjusts tolls based on flow and determines driver behavior using two key functions. First, a "readiness to pay" function estimates the portion of LOVs willing to pay based on toll value and the density difference between general purpose (GP) and HOT lanes. Second, a "willingness to violate" model, grounded in prospect theory, calculates the proportion of drivers likely to misrepresent their occupancy. This violation model accounts for the toll cost saved, the probability of being caught, and the traffic density differential, utilizing value and probability weighting functions to simulate risk-averse decision-making. The model was calibrated using data from the I-10 West corridor, including vehicle counts, speeds, and toll revenue statistics. The study applied this calibrated model to the I-10 West freeway to analyze violation impacts and simulate various operational scenarios. Data analysis confirmed significant discrepancies between FasTrak electronic counts and manual observations, validating the prevalence of toll evasion. The simulations evaluated different demand patterns for LOVs and HOVs, demonstrating how the model captures the interaction between dynamic pricing, driver compliance, and lane congestion. The results illustrate that the toolbox can effectively quantify the effects of enforcement probabilities and toll structures on system performance metrics, such as vehicle miles traveled, delay, and revenue. The significance of this work lies in providing transportation agencies with a robust tool for the quantitative assessment of HOT lane operations. By explicitly modeling violator behavior, the framework allows for the evaluation of enforcement strategies and dynamic pricing policies before field implementation. This supports more effective management of managed lanes, helping to ensure they meet their design goals of maintaining free-flow speeds and optimizing revenue while accounting for realistic, non-compliant driver behaviors.

Key finding

A large number of low-occupancy vehicles misrepresent themselves as high-occupancy vehicles, causing HOT lane congestion and revenue loss that can be modeled by assessing the probability of detection and toll savings.

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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• traffic density