Dynamic Toll Research Articles

Distributed Dynamic Pricing of Multiscale Transportation Networks

We study transportation networks controlled by dynamic feedback tolls. We focus on a multiscale model, whereby the dynamics of the traffic flows are intertwined with those of the routing choices. The latter are influenced by the current traffic state of the network as well as by dynamic tolls controlled in feedback by the system planner. We prove that a class of decentralized monotone flow-dependent tolls allows for globally stabilizing the transportation network around a generalized Wardrop equilibrium. In particular, our results imply that using decentralized marginal cost tolls, stability of the dynamic transportation network is guaranteed around the social optimum traffic assignment. This is particularly remarkable as such dynamic feedback tolls can be computed in a fully local way without the need for any global information about the network structure, its state, or the exogenous network loads. Through numerical simulations, we also compare the performance of such decentralized dynamic feedback marginal cost tolls with constant offline (and centrally) optimized tolls both in the asymptotic and in the transient regime, and we investigate their robustness to information delays.

Investigating the impacts of I-66 Inner Beltway dynamic tolling system

This paper investigates the impacts of the tolling policy on Interstate 66 and its alternative routes in the immediate impact areas. The I-66 inside the Capital Beltway (I-495) is a major corridor connecting Northern Virginia to downtown Washington, D.C. Starting December of 2018, all lanes on this stretch of I-66 transitioned from High-Occupancy-Vehicle (HOV) into High Occupancy/Toll (HOT) in the direction of peak traffic during rush hours. Toll amounts are set dynamically according to congestion levels, and high occupancy vehicles (two people and more) are exempt from the toll. This study first introduces the study area and uses multiple data sources, including historical toll and probe speed, to describe the current traffic conditions on I-66 Inner Beltway and its alternative routes. Additionally, a comprehensive before-after study is provided to evaluate how conversion from HOV into HOT has effectively combated congestion and improved travel time reliability in the study area.

Developing Policy Framework of Dynamic Toll Pricing in India

There are 543 toll plazas across the National Highways and State Highways in India and most of them are currently operating under Manual Toll Collection (MTC) system. The Government of India has recently adopted Electronic Toll Collection (ETC) system over MTC system, but, due to the technical constraints of toll plazas, they are either operating with both ETC and MTC systems or with MTC system only. The motive of switching from MTC to ETC is to reduce congestion and delay time. However, due to the fix toll charges for a vehicle type and heterogeneity in traffic mix, the peak hour traffic causes congestion that ultimately returns to enormous delay to users. The present study proposes the policy framework of Dynamic Toll Pricing (DTP) that alleviate the congestion at toll plazas by consisting of shifting traffic volume from peak/congested hours to off-peak/non-congested hours. This type of pricing proved to be one of the methods to control the traffic by elevating the toll in peak hours and thus giving discounts in off-peak hours. Such type of study about dynamic toll is not yet carried out for Indian scenario where traffic is highly heterogeneous. Hence, the main goal of the present study is to evaluate the DTP for the Indian condition and thus to examine the effect of discount and time saving on the shift in peak hour traffic demand. The observations revealed that most of the respondents are willing to shift in the morning peak hour than evening peak hour. The maximum shift was observed for small cars and for 25 % discount in toll charges. Further, the price elasticities of demand are calculated and are found to vary between -0.03 and -1.19 i.e. with 1 percent of discount the traffic volume changes between 0.03 % to 1.19 %. The output of the present study may set up a framework for DTP in India and may be used for analysing the travel choice behaviour models in future studies.

Dynamic Toll Pricing Models and Traffic Flow Optimization

Dynamic Toll Pricing Models and Traffic Flow Optimization

DwaRa: A Deep Learning-Based Dynamic Toll Pricing Scheme for Intelligent Transportation Systems

In Internet-of-Vehicles (IoV) ecosystems, intelligent toll gates (ITGs) connect nearby metropolitan cities through smart highways. At ITGs, existing solutions integrate blockchain (BC) and deep-learning schemes to leverage trusted and responsive analytics support for connected smart vehicles (CSVs) at ITGs. BC eliminates third-party intermediaries, and secures payments between vehicle owners (VO) and governing authorities (GA). Deep-Learning, on the other hand, facilitates accurate predictions for diverse and complex urban traffic conditions. However, due to fixed toll pricing schemes based on connected smart vehicles (CSV) type, VOs suffer from variable delays at different lanes due to dynamic congestion scenarios. To address the research gaps of such a fixed pricing schemes, we propose a BC-envisioned scheme DwaRa, that operates in three phases. In the first phase, future traffic is predicted based on Markov queues to balance the congestion at different lanes at ITGs efficiently. Then, we propose a novel spatially induced-long-short term memory (SI-LSTM) model to predict current traffic and weather based on historical repositories. Second, based on inputs by the Markov model, SI-LSTM, lane type, and vehicle type, a dynamic pricing algorithm is presented to improve the quality of experience (QoE) of the VO. Finally, based on dynamic price fixation between the VO and the GA, smart contracts (SCs) are executed and transactional data is secured through BC. The proposed scheme is compared against parameters like average mean-squared error (MSE), predicted traffic, scalability, interplanetary file system (IPFS) storage, computation (CC), and communication cost (CCM). At n = 100 test samples, and arrival rate β = 80, the obtained MSE is 0.0012, with a peak average value of 0.00526. The overall CC is 45.88 milliseconds (ms) and CCM is 53 bytes that indicate the proposed scheme efficacy against conventional approaches.

Evaluation of Alternative Methods for Dynamic I-95 Express Lane Pricing

Developing dynamic toll algorithms is a major task for express lanes (EL) policy makers. The majority of pricing methods applied in the field utilize traffic data collected at several points along an EL segment to estimate traffic conditions (mainly traffic density) and calculate tolls. While travel time (TT) data can directly capture field conditions and users’ delay on a segment, such data are rarely utilized to calculate tolls for EL users. The literature suggests minor utilization of TTs in EL toll calculations, but not as the main target performance measure. More importantly, there is a need to look at performance of EL tolls from a multi-criteria perspective where the goal is not only to maintain certain level of service on the ELs. To address these topics, this study tests alternative toll calculation methods that rely mainly on TT data and compare their performance with the currently utilized toll calculation algorithm on I-95 EL in South Florida, U.S. In addition, this study proposes an evaluation framework which includes five different criteria to select the best-performing algorithms: 1. policy, 2. efficiency, 3. capacity utilization, 4. cost recovery, and 5. reliability. Results of the study show that a TT-based algorithm, which is less sensitive than conventional methods, performs similarly to a density-based algorithm, and that a travel time savings (TTS)-based algorithm maintains speeds on the ELs more efficiently than the alternative methods. Future research should apply and validate similar methods on other EL cases around the country.

Dynamic feedback control of day-to-day traffic disequilibrium process

In the literature, networks are usually assumed to operate at their steady states in static traffic assignment models. Given a set of demand management and supply regulation strategies, whether the vehicular traffic on a network would approach or not to a steady state under certain assumptions of behavioral realism is of importance and great interest in practice. Moreover, one primary reason for understanding the long-term traffic evolution dynamics is to control or influence the system trajectory in an efficient manner. To address these problems, we focus on a dynamic feedback control design and its subsequent steady-state analysis for a class of day-to-day (DTD) disequilibrium process so that the network traffic would evolve towards the desired stable traffic equilibrium. We propose an interconnected dynamic system framework for the stability and convergence analysis of DTD traffic adjustment process under the dynamic feedback control. Distinguished from the existing studies, we introduce a notion of output stability as well as the Lyapunov-like conditions to carry out the investigation noting that the steady states of the dynamic tolls that correspond to the Lagrange multipliers of the traffic equilibrium problem can be non-unique. The proposed method gives rise to a new approach to selecting pricing strategy. The proposed method involves only two adjustment parameters with well-identified physical meanings while the requirement on adjustment parameters for convergence and stability is mild. Finally, several numerical examples are presented to illustrate the insightful findings. It is found that, with dynamic feedback control, the network traffic state can converge to the desired equilibrium in a much faster manner than the case without control. The dynamic feedback controller can be implemented as demand management and supply regulation strategies such as road pricing and signal control.

Real-Time Predictive Control Strategy Optimization

Urban traffic congestion has led to an increasing emphasis on management measures for more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with the generation of traffic guidance information using predicted network states for dynamic traffic assignment systems. The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem, which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm that exploits parallel computing is applied to solve this problem. Experiments using a closed-loop approach are conducted on a large-scale road network in Singapore to investigate the performance of the proposed methodology. The results indicate significant improvements in network-wide travel time of up to 9% with real-time computational performance.

Tradable mobility permit with Bitcoin and Ethereum – A Blockchain application in transportation

Blockchain and its underlying technology have intrigued researchers with its promising applications and implications as “the second internet”. There is a wide range of Blockchain applications in transportation and logistics. In this paper, we present the basic principle of cryptocurrencies and Blockchain as well as Ethereum. This is followed by the potential applications of Blockchain in transportation, logistics, and supply chain industries in which the concept of a tradable mobility permit (TMP) to combat traffic congestion is formulated and numerically tested. We then discuss the deployment of the TMP scheme based on a Blockchain platform and its by-products such as dynamic toll pricing, priority for emergency vehicles, heavy truck platooning, as well as connected vehicles.

Big Data Architecture for Intelligent Transport Systems

Day by day as the volume of data is being generated massively, storing of data and processing of data becomes a ever growing challenge in intelligent transport system (ITS). In intelligent transport system there are different areas to concentrate like smart parking systems, dynamic toll charging, smart traffic management etc. This paper is mainly focused on big data architecture for intelligent transport system for dynamic toll charging, traffic management and traffic analysis related data collection from various sources. The data collected from various sources can be in the form of structured data, semi structured data and unstructured data. Because of verity of data collected, this paper gives an idea about which data model is appropriate depending on data collected for transportation system.

System optimum dynamic traffic assignment with departure time choice on two-terminal networks

ABSTRACTThis paper addresses the system optimum dynamic traffic assignment (SO-DTA) problem with departure time choice on a two-terminal network, where the travel cost consists of travel time and an early schedule delay cost. Under a certain condition, we show that SO-DTA reduces to the latest departure earliest arrival flow (LDEAF), and hence methodologies of LDEAF can be used to study SO-DTA. A successive shortest path (SSP) algorithm is used to solve the LDEAF problem. The benefit is that the SSP algorithm involves only the shortest path computations on a static network. System marginal costs, externalities and dynamic user equilibrium tolls are analyzed in the LDEAF context, and used to provide a better understanding of the SO-DTA problem characteristics. Further, the study findings can be used for the morning commute problem as it is a special case of the SO-DTA problem on a two-terminal network.

Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways

Traffic congestion is a huge problem in many countries. It affects not only the inner workings of cities but also the quality of life of the people that endure it. In Portugal, traffic congestion happens mainly on national/urban roads, and this phenomenon has increased since the introduction of the so called shadow-toll systems in highways that were free to use. This work proposes a toll charging system that relies on a novel dynamic congestion charging scheme, supported by state of the art Big Data technologies, in order to shift traffic from national/urban roads to tolled highways, taking into account not only the Quality of Service of the highways and national roads, but also the competitiveness of toll prices for users. This Intelligent Transportation System was tested and validated in a real-world scenario with one of the biggest freight logistics companies in Portugal and with the Portuguese public road infrastructure operator.

Dynamic Toll Pricing using Dynamic Traffic Assignment System with Online Calibration

The paper presents a toll pricing methodology using a dynamic traffic assignment (DTA) system. This methodology relies on the DTA system’s capability to understand and predict traffic conditions, thus enhanced online calibration methodologies are applied to the DTA system, featuring a heuristic technique to calibrate supply parameters online. Improved offline calibration techniques are developed to apply toll pricing in a real network consisting of managed lanes and general purpose lanes. The online calibration methodologies are tested using real data from this network, and the results find the DTA system able to estimate and predict traffic flow and speed with satisfactory accuracy under congestion. Toll pricing is formulated as an optimization problem to maximize toll revenue, subject to network conditions and tolling regulations. Travelers are assumed to make route choice based on offline calibrated discrete choice models. Toll optimization is applied in a closed-loop evaluation framework where a microscopic simulator is used to mimic the real network. Online calibration of the DTA system is enabled to ensure good optimization performance. Toll optimization is tested under multiple experimental scenarios, and the methodology is found able to increase toll revenue compared with the condition when online calibration is not available. It should be noted that the toll rates and revenues presented in this paper are obtained in a simulation environment based on the calibration and optimization algorithms, and as the work is ongoing these results are far from being a recommendation to operators of managed lanes.

A cell-based dynamic congestion pricing scheme considering travel distance and time delay

This study introduces the dynamic congestion pricing (DCP) problem with the consideration of the actual travel distance and time delay (i.e. a joint distance and time-delay toll, JDTDT) in a dynamic network, which is more equitable and effective compared with existing tolling scheme. The system dynamics can be reflected in two aspects: (a) travelers' path choice decisions follow the dynamic user equilibrium principle and (b) the joint distance and time-delay toll takes a time-varying pattern. A multiperiod demand scheme is adopted during the entire modeling horizon and a bi-level programming model for the DCP is formulated to obtain the optimal toll value. Numerical results indicate that the percentage reductions of the minimum total system travel time in the dynamic JDTDT scheme are 6.28%, 4.30% and 7.45% compared to that obtained by the static joint JDTDT, the dynamic joint distance and time toll, and the dynamic pure distance toll, respectively.

Dynamic Toll Charge using openCV and Spark

There are many implementations of intelligent transportation system which is mandatorily required to curb the rising traffic in metropolitan cities. One such implementation is dynamic toll generation which reduces the time taken to pay toll at the toll gates compared to the relatively old method of manual toll collection, although it is still being implemented. One crucial factor to curb traffic and reduce pollution in the cities would be to charge toll according to the seat occupancy in the four wheeler i.e. commuters who use the vehicle appropriately will be charged less and others who use the vehicle luxuriously will be charged more. The system thus implemented produces the toll based on the seat occupancy and the data stored in the databases when the RFID is flashed is used for further analysis.

A Survey on Dynamic Toll Charge

With continuing economic growth, the demand of traffic rises continuously, especially in modern cities like Bangalore. However, the space available in cities is strictly limited. In order to cope with the challenge of serving a rising traffic demand on these limited capacities, traffic management becomes mandatory. One aspect is managing the traffic by charging dynamic toll depends on conditions like occupancy of cab services, previous data. The focus of this project is a dynamic toll pricing scheme that alleviates congestion and maintains an optimized traffic density during peak hour traffic.

Determining Optimal Tolling Rates for Border Regions Using Dynamic Modeling Methods

Many toll facilities have been faced with traffic shortfalls due to inaccurate and over-forecasted toll revenue projections. Therefore calculating optimal toll rates can be a difficult process. Toll rates are often set to reflect the revenue needed to pay back bonds issued to finance the roadway. This research provides an alternative approach to calculating toll rates where revenue can be maximized while still considering the socio-demographics of the region. Several different approaches used in the border region were explored and compared to field data on an existing toll facility in El Paso, Texas. An innovative simulation-based modeling approach was used to test both static and dynamic pricing algorithms. Static tolling results showed optimal toll rates of $0.14/mile and $0.08/mile for Border Highway West in the westbound and eastbound directions respectively. The Cesar Chavez Highway has optimal toll rates of $0.12 and $0.10/mile in the west and eastbound directions. The dynamic tolling approach showed a max toll rate of $1.56/mile for Cesar Chavez Highway (westbound) during the morning peak period and then incrementally decreased to the minimum toll rate. However, the eastbound direction never increased above the minimum toll rate of $0.08 mile. Border Highway West never increased above the minimum toll rate in either direction. The dynamic tolling algorithm prediction is more representative of the optimal tolling rates for the border region-with the exception of Cesar Chavez Highway westbound.

Dynamic pricing for managed lanes with multiple entrances and exits

Priced managed lanes are increasingly being used to better utilize the existing capacity of the roadway to relieve congestion and offer reliable travel time to road users. In this paper, we investigate the optimization problem for pricing managed lanes with multiple entrances and exits which seeks to maximize the revenue and minimize the total system travel time (TSTT) over a finite horizon. We propose a lane choice model where travelers make online decisions at each diverge point considering all routes on a managed lane network. We formulate the problem as a deterministic Markov decision process and solve it using the value function approximation (VFA) method for different initializations. We compare the performance of the toll policies predicted by the VFA method against the myopic revenue policy which maximizes the revenue only at the current timestep and two heuristic policies based on the measured densities on the managed and general purpose lanes (GPLs). We test the results on four different test networks. The primary findings from our research suggest the usefulness of the VFA method for determining dynamic tolls. The best-found objective value from the method at its termination is better than other heuristics for all test networks with average improvements in the objective ranging between 10% and 90% for revenue maximization and 0–27% for TSTT minimization. Certain VFA initializations obtain best-found toll profiles within first 5–50 iterations which warrants computational time savings. Our findings also indicate that the revenue-maximizing optimal policies follow the “jam-and-harvest” behavior where the GPLs are pushed towards congestion in the earlier time steps to generate higher revenue in the later time steps, a characteristic not observed for the policies minimizing TSTT.

A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram

Dynamic congestion pricing has attracted increasing attentions during the recent years. Nevertheless, limited research has been conducted to address the dynamic tolling scheme at the network level, such as to cooperatively manage two alternative networks with heterogeneous properties, e.g., the two-layer network consisting of both expressway and arterial network in the urban areas. Recently, the macroscopic fundamental diagram (MFD) developed by both field experiments and simulation tests illustrates a unimodal low-scatter relationship between the mean flow and density network widely, providing the network traffic state is roughly homogeneous. It reveals traffic flow properties at an aggregated level and sheds light on dynamic traffic management of a large network. This paper proposes a bilevel programming toll model, incorporating MFD to solve the unbalanced flow distribution problem within the two-layer transportation networks. The upper level model aims at minimizing the total travel time, while the lower level focuses on the MFD-based traffic assignment, which extends the link-based traffic assignment to network wide level. Genetic algorithm (GA) and the method of successive average were adopted for solving the proposed model, on which an online experimental platform was established using VISSIM, MATLAB, and Visual Studio software packages. The results of numerical studies demonstrate that the total travel time is decreased by imposing the dynamic toll, while the total travel time savings significantly outweigh the toll paid. Consequently, the proposed dynamic toll scheme is believed to be effective from both traffic and economic points of view.

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.