High-occupancy Toll Research Articles

A comprehensive welfare impact analysis for road expansion projects: A case study

This study provides the overall social welfare implications of key road-capacity expansion options considering free capacity, high occupancy vehicle – HOV, high occupancy toll – HOT, and toll lanes. The broad welfare impacts are estimated in terms of transport users, residents, the regional economy, and private-sector’s welfare. The study uses Dallas County and Tarrant County, Texas, as a case-study region. The study result shows that the HOT option outperforms all other options. It results in an overall increase in social welfare in net present value of $6.7 billion. Apart from that, estimation results do not clearly suggest a preferred option, but instead, depend on the criterion used. The best option in each criterion is: “Do nothing” for investment costs, “HOT” for regional economic impact, “All-tolled” for corridor travel time, “HOT” for network-wide travel time, “Free capacity” for users’ travel costs, “All-tolled” for energy and environmental costs, and “All-tolled” for revenue. The proposed social welfare analysis integrates these conflicting criteria by offering a unified index, and provides the best road-investment option for society.

Dynamic carpool in morning commute: Role of high-occupancy-vehicle (HOV) and high-occupancy-toll (HOT) lanes

This study analyzes carpool in morning commute, using a bottleneck model that allocates a fraction of the bottleneck capacity to a high-occupancy-vehicle (HOV) or a high-occupancy-toll (HOT) lane. Commuters are homogeneous cost minimizers who participate carpool through a platform, either as solo driver, carpool driver or carpool rider. At user equilibrium (UE), no commuter can unilaterally lower her cost by switching to a different departure time, carpool role or road facility. Because a rider can use the in-vehicle time more productively than her driver, she enjoys a cost benefit that grows with the journey time. Therefore, the driver must be compensated more when travel delay due to congestion is longer. This requirement is fulfilled through a time-dependent carpool ratio that determines how much a driver is paid. We characterize and numerically obtain the UE solutions for this dynamic carpool problem. Results of numerical experiments show that the HOV lane promotes carpool and boosts welfare. The larger capacity the HOV lane is allocated, the greater benefit it offers to the system. In addition, an HOT lane can bring additional welfare gains with a modest level of toll.

Mode shift impacts of optimal time-dependent congestion pricing in large networks: A simulation-based case study in the greater toronto area

This paper presents a case study on commuter’s departure time, route and mode choice responses to optimized tolling in the Toronto, Ontario, Canada. The study integrates a toll optimization component into an integrated framework of econometric departure time choice, dynamic traffic assignment model, and random utility maximization (RUM) based mode choice model. An iterative optimization algorithm is used to generate optimal tolling structure which explicitly considers heterogeneous user preferences through econometric departure time choice modelling component. The mode choice component is exogenously retrofitted in an integrated econometric departure time choice modelling component and dynamic operational traffic assignment model to capture individual mode choice behaviour in response to variable congestion pricing. The integrated model results show that individuals are more likely to switch driving routes and departure times than commuting modes when making tradeoffs between schedule delay cost, travel time cost, and toll cost. It is found that the optimal toll scenario improves the network-wide travel time. The modelling framework presented in this study can test a wide range of policy scenarios including the time-variable tolling of congested highways, and high occupancy toll lanes. While the integrated framework presented here is tested at a large scale on the Greater Toronto Area (GTA), we believe the approach can be applied to large-scale networks globally where the objective is seeking the best network-wide spatial and temporal traffic distribution.

A restricted path-based ridesharing user equilibrium

In this paper, a restricted path-based ridesharing user equilibrium (RUE) is proposed to build up the rationales between ridesharing activities and traffic congestion. In the traffic assignment problem with ridesharing, travelers simultaneously choose routes from origins to destinations and travel modes (including solo driver, ridesharing driver, and ridesharing passenger). The proposed RUE model with nonadditive path costs is more realistic than the existing ones based on some additional model specifications: ridesharing drivers bear an extra cost for taking riders and meanwhile receive an extra subsidy, and each rider is carried by only one driver. The Braess paradox is examined within the context of the proposed RUE model. The numerical results on the Braess network with and without high-occupancy toll (HOT) lane show that (a) the existence of the HOT lane will facilitate more travelers’ sharing rides, reduce the number of total vehicles (drivers), and improve the total vehicle travel time (TVTT); (b) the toll charge of solo drivers on HOT lane will promote the ridesharing activities while this effect is conditional on the levels of the extra cost and subsidy. All numerical results on the Braess network and the grid network show that more travelers will participate in ridesharing when the amount of extra subsidy for ridesharing drivers increases.

Factors affecting Atlanta commuters’ high occupancy toll lane and carpool choices

As more high occupancy toll (HOT) facilities are planned and under development, a comprehensive understanding of HOT operations is required for establishing appropriate HOT policies. To enhance the understanding, this study attempts to investigate the factors affecting drivers’ choices on HOT lane use and carpooling in the Atlanta I-85 HOT corridors. The investigation utilizes a questionnaire-based survey addressed to 12,000 households/commuters in the corridors by employing a mail-out/mail-back method. A total of 642 surveys were retrieved and about 300 surveys after data screening were utilized for developing classification tree and logistic regression models that explain commuters’ HOT lane and carpool choices. The estimated models indicate that HOT lane and carpool choices can be affected by various factors including age, gender, income, commute distance, education, number of household workers and car ownership. In addition, the models show that respondents’ positive perception of commute condition changes after the HOT installation can substantially increase the chance of using the HOT lanes. The results also imply that the HOT installation cannot always boost carpool formation, requesting policy makers to develop appropriate strategies that encourage drivers to carpool.

A new design and evaluation approach for managed lanes from a sustainability perspective

This article presents a comprehensive framework that optimizes traffic management measures to reduce emissions and fuel consumption and evaluates their operating and secondary environmental impacts. A new managed lane strategy is presented that minimizes the total passenger travel time on the freeway. This managed lane focuses on simultaneously optimizing toll schemes on the high occupancy toll (HOT) lane and the operation of the general purpose (GP) lanes. With reduced congestion and thus reduced number of acceleration and deceleration events associated with stop-and-go traffic, fuel efficiency increases and emissions are reduced. PARAMICS microscopic traffic simulator, which considers the behavior of individual vehicles (e.g., acceleration, deceleration, and lane changing behavior) is used to collect traffic performance and emission data for estimating mobility and emissions measures (i.e., operating phase). While previous studies only focused on operating phase impact, this study uses a Leontief input-output (I-O) model to establish the financial flow between industries to capture the large-scale environmental impacts of HOT lane deployment (i.e., secondary impact). The core of the new evaluation approach lies in its capability to provide a more thorough assessment of the environmental impacts of traffic management schemes by quantifying the impacts associated with the interplay between the activities of various sectors and the transportation industry. The I-O model is utilized to assess the indirect impacts of induced demand generated from network improvements and evaluate the environmental impacts of HOT lane deployment in regional economies. The developed approach is applied to The City of Calgary. The results of the study show that the traditional approaches that only evaluate the operating phase impacts of transportation strategies considerably overestimate the reduction of greenhouse gas (GHG) emissions.

A Wireless Localization Algorithm With BPNN-MEA-QPSO for Connected Vehicle

As one of key technologies of connected vehicles (CVs) applications, wireless localization can provide accurate and reliable vehicle location for high occupancy tolling and safety critical vehicle applications, such as collision avoidance. Several artificial intelligence methods, such as back propagation neural network (BPNN) and particle swarm optimization (PSO) method, have been employed to optimize the pass-loss model and to improve the accuracy of wireless localization algorithm. However, in view of the stochasticity of initial weights and thresholds in BPNN, it is difficult to reach the global convergence. In this study, a novel double-layer architecture for wireless localization algorithm is proposed based on the optimization of initial weights and thresholds in BPNN and the refinement of search direction and step in PSO algorithm. Based on the architecture, the wireless localization algorithm integrating BPNN with mind evolutionary algorithm (MEA) and quantum-behaved PSO (QPSO) method is proposed and validated using the experiment data in field environment. The validation results show that the proposed localization algorithm has better localization accuracy by comparison with the other localization algorithms, which the average error of the proposed localization algorithm in field environment is about 19 meters. In addition, the localization accuracy shows an improving tendency with the increasing of the number of base stations connected to moving vehicle. The location error is less than 10 meters when the number of base stations connected to the moving vehicle is greater than 7. For the wireless localization in field environment, the accuracy is acceptable for CVs applications.

Dynamic pricing strategy for high occupancy toll lanes based on random forest and nested model

To utilise high occupancy vehicle lanes better, high occupancy toll (HOT) lanes are introduced to counter against congestion on the urban highways. In such system, low occupancy vehicles (LOVs) are allowed to pay a toll and access to HOT lanes from general purpose (GP) lanes, so that the toll rate plays a key role in dynamically allocating LOVs over the HOT and GP lanes to improve the overall system performance. First, this study presents an improved random forest (RF) method to build the lane choice behaviour prediction model. Then, by using the 5 min historical traffic and toll data collected from Interstate 405 in the USA, the improved RF combined with cross-validation and grid search shows the highest accuracy of 88.7%, which is better than other four methods. Furthermore, a novel nested model with two levels is proposed to optimise the toll rates under different real-time traffic conditions. For the nested model, the experimental results show that the proposed dynamic pricing strategy can decrease the total delay and improve the efficiency significantly. To realise the pricing strategy, some Intelligent Transportation System technologies for HOT lane systems are described in detail and designed as the fundamental of the pricing strategy.

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.

Would free park-and-ride with a free shuttle service attract car drivers?

This study examines the potential of free park-and-ride facilities alongside free shuttle services on fast lanes to attract car users. The paper is based on a case study of the “fast lane”, a high-occupancy toll road (HOT) to Tel Aviv, inaugurated on January 2011 in order to ease congestion at the highly congested entrance to Tel Aviv by giving priority to public transportation (busses) and high occupancy vehicles. This unique facility combines a free park-and-ride facility, a fast lane, and a shuttle service running on the fast lane. The aim of our questionnaire-based study was to determine whether this facility creates a viable alternative to private car owners as a means of transport to Tel Aviv. We also sought to identify factors that trigger private-car drivers to shift to the shuttle as a means of transport. Better understanding of these factors may potentially assist transportation experts, planners, and policy-makers in promoting new and better programs to boost public transport. Our results indicate that about 50% of the current facility users shifted from commuting by car, while most of the others shifted from other means of public transportation. Commuters from Tel Aviv’s metropolitan region are the majority of potential users, and the service is mainly required during the morning rush hours. A combination of attributes, including free access to the fast lane, time saving, cost saving, and a relaxed ride, make this service attractive, but time saving is a significant predictor for using the shuttle. However, the results indicate that the facility is also attracting public transportation users to shift to multi-mode travel, where a car is used to commute to the park-and-ride facility.

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.

A Nash equilibrium formulation of a tradable credits scheme for incentivizing transport choices: From next-generation public transport mode choice to HOT lanes

We consider a tradable credits scheme for binary transport games where one option is faster (or more comfortable) than the other, but its quality of service suffers when usage is high. Applications can be found in mode choice (public transit versus road transport), premium (i.e., express bus) versus ordinary public transit, and fast (e.g., high-occupancy toll, or HOT) versus regular lanes on expressways. We are motivated in particular by the choice between public transport and use of the road network as a privilege to be discouraged. In a future where GPS-based time-distance-place road charging exists, such next-generation transport management strategies become realizable as the choice to drive or not can be linked to a fixed fee toll, or indeed to a tradable credits scheme. When public transport payment uses the same smart card as the road usage fee (via tolls or tradable credits) usage of the two may be linked. In this setting, a public transport vs. road-use tradable credit scheme becomes feasible. In this case, individuals wishing to choose the faster option must obtain credits from other commuters via credit trading, rather than pay a direct toll or fee. Such a scheme creates a kind of equity, in the sense that lower-income commuters have an economic incentive to resort to the slower or less comfortable choice. We study the underlying market and its effects on individuals’ utilities; we use an atomic game framework so as to model explicitly the exchange process across users. The market we define determines the quantities of users choosing each option, as opposed to the prices themselves. Using the properties of potential games, we show that under mild assumptions, efficient Nash equilibria exist and can be reached using simple learning algorithms. We show that these equilibria can satisfy the transport authority’s requirements, and thus drive the transport system to a state where a desired proportion of individuals resort to each of the two options, when the scheme’s parameters are well tuned.

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.