High-occupancy Toll Lanes Research Articles

Choice of High Occupancy/Toll Lanes under Alternative Pricing Strategies

High occupancy/toll (HOT) lanes typically vary tolls charged to single occupant vehicles, with the toll increasing during congested periods. The toll is usually tied to time of day or to the density of vehicles in the HOT lane. The purpose of raising the toll with congestion is to discourage demand sufficiently to maintain travel speeds in the HOT lane. However, it has been demonstrated that the HOT toll may act as a signal of downstream congestion (in both general purpose (GP) and HOT lanes), causing an increase in demand for the HOT lane, at least at lower prices. This paper develops a model of lane choice to evaluate alternative HOT lane pricing strategies, including the use of GP density, to more accurately reflect the value of the HOT lane. In addition, the paper explores the potential effect these strategies would have on the HOT lane vehicle share through a partial equilibrium analysis. This analysis demonstrates the change in demand elasticity with price, showing the point at which drivers switch from a positive to negative elasticity.

A link-node reformulation of ridesharing user equilibrium with network design

Though the conventional network design is extensively studied, the network design problem for ridesharing, in particular, the deployment of high-occupancy toll (HOT) lanes, remains understudied. This paper focuses on one type of network design problem as to whether existing roads should be retrofit into HOT lanes. It is a continuous bi-level mathematical program with equilibrium constraints. The lower level problem is ridesharing user equilibrium (RUE). To reduce the problem size and facilitate computation, we reformulate RUE in the link-node representation. Then we extend the RUE framework to accommodate the presence of HOT lanes and tolls. Algorithms are briefly discussed and numerical examples are illustrated on the Braess network and the Sioux Falls network, respectively. Results show that carefully selecting the deployment of HOT lanes can improve the overall system travel time.

Hybrid model predictive control based dynamic pricing of managed lanes with multiple accesses

We propose a hybrid model predictive control (MPC) based dynamic tolling strategy for high-occupancy toll (HOT) lanes with multiple accesses. This approach preplans and coordinates the prices for different OD pairs and enables adaptive utilization of HOT lanes by considering available demand information and boundary conditions. It also addresses such practical issues as prevention of recurrent congestion in HOT lanes, ensuring no higher toll for a closer toll exit and fairness among different OD groups at each toll entry, as well as the fact that high occupancy vehicles (HOVs) have free access to the HOT lanes. Taking the inflows at each toll entry as the control, traffic densities and vehicle queue length as observed system states, and boundary traffic as predicted exogenous input, we formulate a discrete-time piecewise affine traffic model. Optimal tolls are then derived from a one-to-one mapping based on the optimal toll entry flows. By properly formulating the constraints, we show that the MPC problem at each stage is a mixed-integer linear program and admits an explicit control law derived by multi-parametric programing techniques. A numerical experiment is presented for a representative freeway segment to validate the effectiveness of the proposed approach. The results show that our control model can react to demand and boundary condition changes by adjusting and coordinating tolls smoothly at adjacent toll entries and drive the system to a new equilibrium that minimizes the total person delay. Under the optimal prediction horizon, the on-line computational cost of the proposed control model is only about 4% and 8% of the modeling cycle of 30 s, respectively, for two typical traffic scenarios, which implies a potential of real-time implementation.

A time-varying parameters vector auto-regression model to disentangle the time varying effects between drivers’ responses and tolling on high occupancy toll facilities

High Occupancy Toll (HOT) lane systems are considered one of most effective countermeasures to mitigate freeway congestion. Existing studies have largely focused on developing optimal tolling strategies to maximize the benefits of congestion pricing. Limited effort has been made to model the dynamic feedback mechanism of drivers’ responses to tolling. A thorough understanding of how the interactive relationship between demands (in both HOT lane and general purpose lanes) and toll rates evolves over time is necessary. The underlying mechanism can be used directly for guiding future HOT facilities investment decisions. This study builds upon the traditional vector autoregressive model and enables its parameters to be time-varying. Such a relaxation, namely, time-varying parameter vector autoregressive model (TVP-VAR), is used to answer the following two questions: (1) Is there a time varying effect between general purpose lane volume, HOT lane volume and dynamic toll rate? (2) If there is, how to quantify such time-varying interdependencies? Based on the empirical data from loop detectors and toll logs on Washington State Route 167 (SR167), we identified the existence of time-varying effects between drivers’ responses and toll rates, and quantified the evolving interactions amongst HOT demand, general purpose demand and tolling via time-varying impulse responses. In addition, we found that drivers’ perceptions on HOT lanes across distinct geographical locations are significantly different.

Analysis of factors affecting willingness to pay for high-occupancy-toll lanes: Results from stated-preference survey of travelers

This study examines the feasibility of implementing high-occupancy toll (HOT) lanes in a Middle Eastern country to obtain an improved understanding of such a policy and how it may affect the behavior of travelers in this region. This study focuses on the relationship between travelers' willingness to pay (WTP) for HOT lanes and different factors, such as traveler characteristics and trip conditions. A combination of the stated preference and revealed preference surveys was used to collect data, and the analysis is based on approximately 6000 responses. Urgency, speed, and distance are three trip conditions that were utilized to determine drivers' WTP to use HOT lanes to avoid congestion. The influence of various traveler characteristics on drivers' WTP was also examined. The results indicated that WTP decreases as trip conditions improve. Furthermore, the analysis revealed that drivers' WTP to avoid congestion is affected by their income, trip frequency, nationality, and number of vehicles owned.

A probability-based indicator for measuring the degree of multimodality in transportation investments

Although decision makers may favor a “multimodal” transportation system, it can be difficult to indicate the extent to which a given transportation investment is, or is not, multimodal. This lack of an indicator can be acute when the project selection process requires consideration of how a given investment supports increased multimodality. In response to this need, this research reports on a taxonomy for classifying the degree of multimodality for transportation projects. Probability theory was employed with principal component analysis to create a new indicator based on both demand (modal shares) and supply (monetary investment for each mode). The indicator offers three main benefits in the area of performance measurement: (1) it is applicable in cases when some data are missing; (2) it provides a way of comparing multimodality from diverse projects such as high-occupancy toll lanes or multimodal centers; and (3) it can help decision-makers quantify how multimodality has changed over time. For example, application of the indicator to six U.S. public-private partnership projects in Colorado, Florida, Rhode Island, and Virginia showed that the degree of multimodality increased by an average value of 57%. (While the manner in which the impact boundary is defined affects this calculation for specific projects, the average value remained relatively stable whether the impact boundary was equal to the average commute trip length or less than half that amount.) Given that some planners view multimodality as societally beneficial, the indicator proposed herein can help one evaluate the multimodal potential of proposed transportation investments.

Understanding the emission impacts of high-occupancy vehicle (HOV) to high-occupancy toll (HOT) lane conversions: Experience from Atlanta, Georgia

ABSTRACTConverting a congested high-occupancy vehicle (HOV) lane into a high-occupancy toll (HOT) lane is a viable option for improving travel time reliability for carpools and buses that use the managed lane. However, the emission impacts of HOV-to-HOT conversions are not well understood. The lack of emission impact quantification for HOT conversions creates a policy challenge for agencies making transportation funding choices. The goal of this paper is to evaluate the case study of before-and-after changes in vehicle emissions for the Atlanta, Georgia, I-85 HOV/HOT lane conversion project, implemented in October 2011. The analyses employed the Motor Vehicle Emission Simulator (MOVES) for project-level analysis with monitored changes in vehicle activity data collected by Georgia Tech researchers for the Georgia Department of Transportation (GDOT). During the quarterly field data collection from 2010 to 2012, more than 1.5 million license plates were observed and matched to vehicle class and age information using the vehicle registration database. The study also utilized the 20-sec, lane-specific traffic operations data from the Georgia NaviGAtor intelligent transportation system, as well as a direct feed of HOT lane usage data from the State Road and Tollway Authority (SRTA) managed lane system. As such, the analyses in this paper simultaneously assessed the impacts associated with changes in traffic volumes, on-road operating conditions, and fleet composition before and after the conversion. Both greenhouse gases and criteria pollutants were examined.Implications: A straight before-after analysis showed about 5% decrease in air pollutants and carbon dioxide (CO2). However, when the before-after calendar year of analysis was held constant (to account for the effect of 1 yr of fleet turnover), mass emissions at the analysis site during peak hours increased by as much as 17%, with little change in CO2. Further investigation revealed that a large percentage decrease in criteria pollutants in the straight before-after analysis was associated with a single calendar year change in MOVES. Hence, the Atlanta, Georgia, results suggest that an HOV-to-HOT conversion project may have increased mass emissions on the corridor. The results also showcase the importance of obtaining on-road data for emission impact assessment of HOV-to-HOT conversion projects.

Macroscopic and microscopic analyses of managed lanes on freeway facilities in South Florida

Abstract As congestion grows in metropolitan areas, agencies tend to utilize managed lanes on their freeway systems. Managed lanes have several forms and names, such as high-occupancy vehicle (HOV) lanes, high-occupancy toll (HOT) lanes, express lanes, and bus-only lanes. Although managed lanes have received significant attention as they increased the overall throughput and improved mobility without adding more lanes, little has been known about their operational capabilities. In addition, calibrating managed lane facilities can be challenging as they do not necessarily follow the same behavior with general purpose freeway lanes. This paper presents an operational analysis of two HOT lane segments located in South Florida. The sites are one-lane and two-lane segments separated by flexible pylons (FPs). The paper includes a macroscopic capacity analysis, and a microscopic calibration of the two sites using VISSIM microsimulation. The research findings assist in determining the capacity and speed-flow relationship of these segments, and also provide guidance for microsimulation model calibration for practitioners. The results of the study indicate that the percent drop in capacity for the one-lane FP site is 7.6% while the flow did not substantially change after the breakdown in the two-lane FP site. The research findings also include guidelines for simulating the breakdown events and calibrating one-lane and two-lane managed lane facilities in VISSIM microsimulation software. The Wiedemann car-following parameters (CC0 = 3.9 ft, CC1 = 1.9 s, CC2 = 26.25 ft, CC4 = −0.35, and CC5 = 0.35) provided the best fit for the one-lane FP site, while the combination (CC0 = 4.92 ft, CC1 = 1.9 s, CC2 = 39.37 ft, CC4 = −0.7, and CC5 = 0.7) parameters is recommended for the two-lane FP site.

Ridesharing User Equilibrium and Its Implications for High-Occupancy Toll Lane Pricing

Shared mobility—ridesharing in particular—has become an important research topic in recent years because of the ability to relieve traffic congestion, reduce travel costs, and reduce energy consumption. However, researchers in transportation science still lack an understanding of how to incorporate ridesharing into the transportation planning process, specifically in the traffic assignment problem. This paper presents ridesharing user equilibrium (RUE) as a path-flow–based nonlinear complementarity problem with side constraints. This formulation is extended by considering the presence of high-occupancy toll lanes. A numerical example is given to illustrate the relationships of the path cost coefficients on RUE and on the occurrence of the Braess paradox. The performance of two tolling strategies is compared: one charges single-occupancy vehicles, and the other charges both single-occupancy vehicles and vehicles carrying only one passenger.

Using Google’s Aggregated and Anonymized Trip Data to Support Freeway Corridor Management Planning in San Francisco, California

With rising urban freeway congestion and limited funds available for highway expansion, it may be essential to manage traffic growth by using high-occupancy toll lanes and other travel demand management (TDM) measures. To prepare for and help guide freeway corridor management planning in the US-101 and I-280 corridors in San Francisco, California, information describing trip origins and destinations by time of day was desired. Observed roadway facility-specific origin–destination (O-D) flows can help researchers to understand spatial distribution of demand and impute willingness to pay, actions that are useful in evaluating various TDM strategies. This paper describes a new passively collected O-D data source—Google’s aggregated and anonymized trip (AAT) data—obtained under Google’s Better Cities program. Aggregate hourly flow matrices for 85 districts covering California’s nine-county Bay Area specific to four freeway segments in San Francisco were obtained. Because AAT data account for only a sample of travelers, Google provides relative flows rather than absolute counts. Linear regression models were estimated to relate relative flows in the AAT data set and observed traffic volumes from the California Department of Transportation’s Performance Measurement System. The models were applied to convert relative flows to trips and derive facility-specific, time-dependent O-D matrices. Comparison of these facility-specific O-D matrices to select link O-D matrices from a regional travel demand model show that there is a higher correlation in terms of productions at origin districts and attractions at destination districts than at the O-D flow level. Some opportunities and limitations of the new data source are discussed, along with recommendations for future research.

Design of income-equitable toll prices for high occupancy toll lanes in a single toll facility

ABSTRACTIncome inequity potentially exists under high occupancy toll (HOT) lanes whereby higher-income travelers may reap the benefits of the facility. An income-based multi-toll pricing approach is proposed for a single HOT lane facility in a network to maximize simultaneously the toll revenue and address the income equity concern, while ensuring a minimum level-of-service on the HOT lanes and that the toll prices do not exceed pre-specified thresholds. The problem is modeled as a bi-level optimization formulation. The upper level model maximizes revenue for the tolling authority subject to pre-specified upper bounds on tolls. The lower level model solves the stochastic user equilibrium problem. An agent-based solution approach is used to determine the toll prices by considering the tolling authority and commuters as agents. Results from numerical experiments indicate that a multi-toll pricing scheme is more equitable and can yield higher revenues compared to a single toll price scheme across travelers.

Application of Bayesian Stochastic Learning Automata for Modeling Lane Choice Behavior on High-Occupancy Toll Lanes on State Road 167

This paper investigates the learning behavior of users of State Road 167 high-occupancy toll lanes by use of toll transaction data collected over a 6-month period. The Bayesian stochastic learning algorithm theory was used to model drivers’ sequential lane choice decisions. Reward and penalty parameters were used to update users’ lane choice probabilities. The results showed that the effect of reward parameters that increased the probability of selection of an alternative after a satisfactory experience was more obvious than the effect of penalty parameters that decreased the probability of selection of an unfavorable choice. Low magnitudes of learning parameters might indicate strong habit formation of users. Moreover, the posterior distributions of learning parameters indicated that user perception heterogeneity existed when the outcomes of choices were evaluated. Finally, user familiarity was investigated with a subsample of less experienced users, and it was shown that the learning rates of more familiar users were lower than those of less familiar users.

Microsimulation-Based Real-Time Congestion Pricing Strategy for Managed Lane

A real-time toll that accurately reflects the congestion and delays of a system enhances a high-occupancy toll (HOT) lane system. This paper extends an existing system-optimal real-time congestion pricing strategy by relaxing the assumption of constant capacity, which allows one to implement the strategy on a dynamic microsimulation of the HOT lane, whose capacity and that of general purpose lanes vary because of the weaving activity at the end of the HOT lane. It was found that the proposed model was superior in total delay savings, particularly when bottleneck capacities varied significantly. A clear linear relationship was also found between the pricing coefficient and dimensionless delays and the pricing coefficient and revenue. These findings strengthen the system-optimal linear toll pricing strategy and suggest that departments of transportation can implement practical toll systems that incorporate the real-time discharge rates of bottlenecks.

Improving Decision Making for Transportation Capacity Expansion: Qualitative Analysis of Best Practices for Regional Transportation Plans

The need for innovative regional transportation planning has grown as metropolitan areas consider the impact of congestion reduction efforts on induced demand, public health, and fossil fuel use and climate change. Although conventional practice among metropolitan planning organizations (MPOs) is to simply expand roadway capacity to relieve congestion, many MPOs have also developed new solutions. This study qualitatively analyzes a national sample of 38 regional transportation plans to identify best practices among MPOs for increasing the capacity of regional transportation networks without inducing additional traffic or unnecessary emissions. It focuses on real-world examples of innovative practices such as the use of high-occupancy toll lanes on major freeways, regulations and ordinances designed to improve the connectivity of minor streets, management of transit corridors, and the best locations for bicycle and pedestrian infrastructure.

Proof-of-concept for methodological framework to evaluate operational strategies with travel demand models

Recently, limited available resources for physical capacity expansion have generated supports for short-term operational improvements. Yet, only a few studies have dealt with evaluating these operational strategies effectively within the traditional transportation planning process even though suitable operational strategies impact to not only specific corridors or regions but also the whole transportation network. This is because it is generally perceived that integrating travel demand models with operational analysis approaches is quite difficult due to different constraints, modeling structures, and required data sets. In this regard, the concept of methodological framework to evaluate operational strategies with travel demand models is developed and validated by the proper case study (i.e., high occupancy toll lanes deployment in the Hampton Roads area in Virginia, US) in this research. The proposed framework consists of three major components: (i) the selection of an appropriate operational analysis approach, (ii) the disaggregation of daily traffic volumes to peak period volumes, and (iii) the alignment of modeling elements between the travel demand model and operational tool. Key contributions from this research are that (i) the proposed methodology enables the evaluation of travel behavioral changes without microscopic simulation, especially in terms of capturing network flow pattern changes caused by behavioral shifts after operational strategy deployment, (ii) the proposed framework eliminates assumptions required when only operational tools are used to evaluate operational strategies, (iii) the disaggregation method of a daily trip distribution matrix into peak period matrices by using survey data are developed, (iv) specific details influencing integration in terms of data types, peak period link capacity, volume-delay functions, and link impedance are identified. Consequently, even though this research still has some limitations (e.g., inherent weakness of travel demand models), this can be a starting point to develop more detailed guidelines as well as a good reference for practitioners and researchers who wish to evaluate operation strategies within transportation planning process.

Optimal Deployment of Managed Lanes in General Networks

Falling budget, political, and environmental constraints force decision makers and transportation authorities to develop innovative ways to better manage existing facilities. Managed lanes, more specifically, high-occupancy vehicle (HOV) and high-occupancy toll (HOT) lanes, have been recognized as viable options to improve vehicle trips without constructing additional capacity. This paper investigates how to deploy managed lanes in general networks. A mathematical program with complementarity constraints is formulated to simultaneously determine the locations of managed lanes and the toll rates on HOT lanes to maximize the social benefit. Numerical examples based on the networks in the literature are presented to verify the model.

High-Occupancy Toll Lane Decision Making: Income Effects on I-85 Express Lanes, Atlanta, Georgia

The I-85 express lanes were the first value pricing facility in the Atlanta, Georgia, metropolitan region. This study investigated the behavior and decision making of users and nonusers of those high-occupancy toll (HOT) lanes. The study used a unique combination of express lane data and household demographic data, obtained from a private marketing firm, to examine decision making at the trip level for users from different income segments and demographic clusters. Trip characteristics (e.g., toll amount and trip length, operating conditions on HOT and general purpose lanes) and socioeconomic characteristics (e.g., household income and size) were incorporated into the choice models. This study also provided a summary of express lane use characteristics by income segments and investigated trip speeds by lane types and income categories. Lane choice decisions were modeled with binary logit methods to examine potential determinants of HOT lane use. Households were segmented into lower-, medium-, and higher-income groups to illustrate the differences in responses across these categories. In addition, households were clustered by their demographic characteristics to compare lane choice determinants across similar households. Elasticities for the various factors were calculated. The results showed slightly higher rates of HOT use among the higher-income segment, inelastic demand sensitivities for most factors, and few differences between lane choice determinants across income groups. With goodness-of-fit values that ranged from .18 to .21, these disaggregate models left most lane choice variation unexplained.

Exploring a Toll Auction Mechanism Enabled by Vehicle-to-Infrastructure Technology

High-occupancy toll (HOT) lanes—an increasingly popular solution for congested roadway networks—give drivers the option to access express lanes. The cost of entry often varies with demand, although no standard method of optimizing these price points exists. Using the principles of a Vickrey auction that incentivizes true-value bids, this paper proposes a tolling system that uses vehicle-to-infrastructure technology to optimize toll operator revenue with HOT lane usage. In the scenario, a roadway network consists of a HOT lane and a general-purpose lane, each with identical physical properties. Drivers can access the HOT lane at the start of the facility or at one interim point along the roadway. With the use of a triangular distribution to approximate the distribution of travelers’ value of time (VOT), the model explores the impact of varying the distribution's mode on revenue earned by the toll operator. Results from the model indicate that when the toll operator maximizes the model's revenue, the percentage of auction bids accepted for toll road access is robust to changes in the VOT distribution. This percentage equates to approximately 17% of vehicles accessing the facility. Given the difficulty in obtaining actual travelers’ distribution of VOT, this auction tolling mechanism implies that obtaining an exact VOT distribution may not be necessary for this type of tolling analysis.

Incorporating Departure Time Choice into High-occupancy/toll (HOT) Algorithm Evaluation

This paper presents an evaluation framework for high-occupancy/toll (HOT) lanes, extending previous frameworks by incorporat- ing departure time choice alongside stochasticity in demand. Drivers are divided into two classes, strategic and captive drivers. Strategic drivers time their departures to minimize expected generalized cost and can choose either the free lanes or HOT lane. Captive drivers have random departure times, and must choose the free lanes. Generalized cost for strategic drivers takes the form of the well-known schedule delay penalty function combined with travel time. The traffic flow model involves two parallel bot- tlenecks, one for each lane group. Four toll algorithms are compared: a fixed toll which is constant in time and unresponsive; a dynamic, but unresponsive toll based on expected demand; a dynamic and responsive toll which adapts based on congestion in the HOT lane; and a “full-information” toll which is aware of all captive driver inflows.

Value of Schedule Delays by Time of Day

This study estimated the value of schedule delays (VSD) along the high-occupancy toll (HOT) lanes on State Road 167 in the state of Washington on the basis of revealed preference data from toll-paying users. More than 5 months of tolling records that included more than 1 million lane choices were used in the analysis. A mixed logit model was estimated to account for heterogeneity at the individual level. Drivers were assumed to evaluate reliability relative to their desired or expected arrival times (i.e., reference points). Three empirically determined reference point specifications were employed to calculate schedule delays, and the sensitivity of the results with respect to reference points was reported accordingly. The model estimations showed significant variations in the estimated VSDs for different times of day and also in different travel directions. The differences in VSD estimations could be as high as $17/h in a single time period depending on the reference point assumption. Reference point assumption was also shown to have a significant effect on the VSD when scheduling delays were used for measuring travel time reliability. Empirical findings of this study provide useful insights into the variability of the value of travel time of users traveling in different time periods. In particular, these results can help in the development of policies for more effective allocation of traffic capacity to HOT lanes, especially during peak congestion periods.