A New Approach to Calculating Dynamic Pricing of High-Occupancy-Toll (HOT) Lanes

Research Lead: Wenlong Jin

UC Campus(es): UC Irvine

Problem Statement: In the U.S., high-occupancy-vehicle (HOV) lanes are widely used on freeways to reduce congestion since the 1970s. Such lanes are reserved for cars with a minimum of two or three occupants and other qualified vehicles. Under some conditions, however, the HOV lanes are underutilized when the whole freeway system is congested. High-occupancy-toll (HOT) lanes have been one of the most successful lane management methods, which combine HOV lanes and congestion pricing strategies by charging single occupancy vehicles (SOVs) to use HOV lanes during peak periods. HOT lanes offer numerous benefits to both the operators and users by making use of the excess capacity on HOV lanes and have been implemented on State Route 91, US Route 101, and I-15 in California and other places. Generally, the operational goals for the HOT lanes are two-fold: (i) maintaining the freeflow condition; and (ii) maximizing the usage of the HOV lanes. This will help to guarantee the trip time reliability of both HOVs and paid SOVs as well as to minimize the congestion level on the general purpose (GP) lanes. The success of achieving these goals is predicated on the determination of prices dynamically, which in turn depends on an understanding of the values of time (VOTs) of SOVs. Thus, two outstanding problems are to determine a dynamic price for time-dependent demands of both HOVs and SOVs and to estimate SOVs’ VOT.

Project Description: This project aims to develop more effective ways to set HOT lane prices in real-time, ensuring they are used efficiently and provide reliable travel times for all drivers. The research makes three fundamental contributions: (i) presents a simpler formulation of the point queue model based on the new concept of residual capacity, (ii) proposes a simple feedback control theoretic approach to estimate the average value of time and calculate the dynamic price, and (iii) analytically and numerically proves that the closed-loop system is stable and guaranteed to converge to the optimal state, in either Gaussian or exponential manners.

Status: Completed

Budget: $75,000