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California has set the goal of being carbon neutral by 2045 to prevent the worst impacts of climate change. Transportation continues to be the largest source of greenhouse gas emissions in the state and even with a shift to zero-emission vehicles, the path to climate neutrality requires a reduction in per-capita Vehicle Miles Traveled (VMT). To meet these goals, various road pricing strategies have been proposed and are being implemented across the state. This report aims to develop a comprehensive understanding of what road pricing can and cannot achieve, and how different projects align with the state’s goals of reducing VMT and greenhouse gas emissions. The study involved interviews with 14 officials from city, regional, and state agencies, and analyzed case studies of existing and proposed road pricing projects.Findings indicate that road pricing can effectively reduce congestion along a corridor and generate revenue. However, differences in perspectives exist between state and local agencies regarding success metrics, equity impacts, and revenue allocation. State agencies prioritize VMT and greenhouse gas reductions, while local agencies tend to focus more on operational performance and travel time reliability. Key recommendations to overcome these goal discrepancies include pricing existing lane capacity, increasing government transparency, and investing revenue in alternatives to car travel, particularly to help low-income households. Road pricing represents a promising strategy to address transportation system challenges in California, but will require more inter-agency coordination and clear policy direction from state leaders.

• This study examines the impact of congestion charging on private car ownership. • A quasi-natural experiment design is applied through spatial difference-in-differences. • The London Congestion Charge is used as a case study. • An Average Treatment Effect on the Treated of 6.95% ownership reduction is identified. • The identified impact is useful in the design or augmentation of congestion charging policies. [ABSTRACT FROM AUTHOR]

Herd effect is a widespread phenomenon in real-world situations. This study explores how the herd effect can be used to manage network flow effectively. We examined its impact on travelers' route choices and propose a mixed network flow evolution process that incorporates the herd effect, considering two types of travelers: those who receive route subsidy information and those who do not. Based on this evolution process, we developed a bi-level optimization model to determine the optimal subsidized routes, the subsidy amounts per kilometer, and the proportion of travelers receiving subsidy information. A hybrid algorithm with two iterative procedures was proposed to solve the model, in which the adaptive genetic algorithm (AGA) was employed to solve the upper-level nonlinear mixed-integer programming problem, and the partial linearization method was used to solve the lower-level network flow evolution process. Numerical results indicate that the presence of herd effect can effectively reduce both the total travel time of the network and the overall subsidy costs. The findings of this study have significant implications for the utilization of the herd effect in designing navigation software and developing congestion pricing strategies. [ABSTRACT FROM AUTHOR]

In 2008, approximately one third of Beijing's severe smog pollution stemmed from its traffic sector. The local government addressed this problem by adopting a city-wide driving restriction policy. Starting from 2010, a congestion charge policy has been under discussion. While Beijing's municipality has tried to bring traffic-related smog under control with these regulatory and market-based policies, there has been significant public opposition to both policies. Public acceptance is critical to successful policy making and implementation; however, the literature offers little insight into the key elements influencing public acceptance in the context of multiple policy choices. Based on a questionnaire survey of 285 respondents in Beijing, this paper adopts ordered logistic regression to explore the key factors influencing public acceptance of driving restriction and congestion charge policies. The results show that the public in Beijing prefers the driving restriction policy to the congestion charge policy. Political concerns, such as concerns relating to policy making transparency and perceived fairness, as well as economic concerns, such as concerns relating to car ownership and extra traffic cost, are essential determinants of public acceptance of such policies. Specifically, citizens who are more concerned about equity hold stronger negative opinions on the congestion charge policy. [ABSTRACT FROM AUTHOR]

Congestion pricing is one of the efficient travel demand management strategies. Many existing researches focus on dealing with the toll optimization problem for a single area. However, the urban network is often composed of several administrative regions. Furthermore, even inside a single administrative region, there may be multiple subnetworks with different traffic dynamics. As a result, the centric pricing scheme may not be applicable. This paper aims to design a coordinated dynamic pricing scheme for such a scenario with multiple adjacent areas which experience an overlapping congested period. Unlike the traditional approach centered on the bi-level mathematical programming, we adopt the regressing Kriging model to estimate the input–output mapping, thus searching for the simulation-based optimal solution in the toll design problem. Two types of coordinated pricing schemes are proposed. The first or unconstrained scheme only focuses on the network performance, while the second or constrained scheme further takes into account the pricing efficiency. The proposed coordinated pricing scheme is further compared with the perimeter control. The results indicate that our scheme is more moderate without imposing traffic burden on the links/corridors heading to protected zones. [ABSTRACT FROM AUTHOR]

Two commonly used methods for pricing managed lanes (MLs) are adjusting tolls based on the time of day or dynamically changing them in response to real-time traffic congestion. It is unclear which of these tolling options is more effective in controlling the usage of MLs. This study employed nine different performance measures with the goal to evaluate the toll's capacity to manage traffic on six MLs across the United States, including if dynamic tolling or variable tolling is better at managing ML traffic. Among the MLs examined, two were priced variably (SR-91 and I-25), while four were priced dynamically (I-35W, I-394, I-35E, and MoPac). Overall, both pricing approaches were found to effectively maintain traffic flow on MLs, with neither method demonstrating clear superiority. However, dynamic pricing did exhibit a slight advantage over variable pricing in most performance measures. Our results imply that both pricing mechanisms are similarly effective in ensuring the smooth operation of MLs and are therefore viable options for ML operators.

Transportation pricing policies aim to manage vehicular demand for parking, dense urban areas, roadways, and highway lanes. Although pricing policies take various forms, most were designed in a world before the sharing economy and ride-sourcing companies. Hence, the efficacy of existing pricing policies in a world with shared mobility services requires further consideration. Moreover, future pricing policies designed to handle private vehicles and shared ride-sourcing vehicles must consider the behavior of both sets of travelers and vehicle fleets. This study develops a conceptual framework to support systems level analysis of pricing policies for a world with private and shared vehicle usage. It qualitatively analyzes the impact of shared vehicles on the effectiveness of various pricing policies, while also considering the role of vehicle-to-infrastructure technology. This conceptual framework will support future research that uses activity-based travel demand and dynamic network assignment models to evaluate congestion pricing policies in an era of shared mobility. Additionally, the study presents a detailed review of the literature related to transportation pricing together with a trend analysis on congestion pricing policies in Transportation Research Board annual meeting titles and abstracts.

Modern urban centers have one of the most critical challenges of congestion. Traditional electronic toll collection systems attempt to mitigate this issue through pre-defined static congestion pricing methods; however, they are inadequate in addressing the dynamic fluctuations in traffic demand. Dynamic congestion pricing has been identified as a promising approach, yet its implementation is hindered by the computational complexity involved in optimizing long-term objectives and the necessity for coordination across the traffic network. To address these challenges, we propose a novel dynamic traffic congestion pricing model utilizing multi-agent reinforcement learning with a transformer architecture. This architecture capitalizes on its encoder-decoder structure to transform the multi-agent reinforcement learning problem into a sequence modeling task. Drawing on insights from research on graph transformers, our model incorporates agent structures and positional encoding to enhance adaptability to traffic flow dynamics and network coordination. We have developed a microsimulation-based environment to implement a discrete toll-rate congestion pricing scheme on actual urban roads. Our extensive experimental results across diverse traffic demand scenarios demonstrate substantial improvements in congestion metrics and reductions in travel time, thereby effectively alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

Prior studies have examined various economic, environmental, and social impacts of the London congestion charge (LCC). However, few studies have investigated how the Transport for London (TfL) managed the LCC in all stages of its policy. Without active, strategic management of policies, stakeholders affected may doubt the policies' legitimacy and reduce policy effectiveness. Thus, examining the means utilized to achieve effective policy management is critical and can potentially influence the development and management of future policies. This study adapts Mitchell et al.'s stakeholder typology to study how the TfL, as part of the management of, and as the dominant stakeholder in, environmental regulations in London, strategically managed the LCC via their own efforts to exert power, establish legitimacy, and claim urgency. Relying on Yin, we analyze publicly available data to show how the TfL actively managed the LCC to ensure its sustainability as policy. The TfL primarily used tactics to maintain and enhance the LCC's legitimacy with authoritative power and fears of unproven urgency to influence other stakeholders. Results have practical implications for policymakers contemplating policy reforms and shed additional light on less‐discussed latent factors regarding policy management. This study contributes to the literature by applying stakeholder theory to the domain of policy reform, administration, and management. Our findings have policy implications as policymakers may benefit from learning the tactics examined in this study to assess their policy management and administration. Lastly, through our discussion and conclusions, we reflect on our findings to encourage research on policy management. [ABSTRACT FROM AUTHOR]

We estimate an equilibrium sorting model of housing location and commuting mode choice with endogenous traffic congestion to evaluate urban transportation policies. Leveraging fine-scale data from travel diaries and housing transactions identifying residents' home and work locations, we recover rich preference heterogeneity over both travel mode and residential location decisions. While different policies produce the same congestion reduction, their impacts on social welfare differ drastically. In addition, sorting undermines the congestion reduction under driving restrictions and subway expansion but strengthens it under congestion pricing. The combination of congestion pricing and subway expansion delivers the greatest congestion relief and efficiency gains. (JEL H76, O18, P25, R23, R31, R41, R48) [ABSTRACT FROM AUTHOR]

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users' willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations. [ABSTRACT FROM AUTHOR]

This study examines the effects of seasonal congestion pricing on disc golf participation at Heckscher State Park using scorekeeping app data from January 1, 2015, to December 31, 2023. By comparing daily visits on fee and non -fee days and accounting for weather, holidays, and regional disc golf activity, we isolate the impact of pricing on recreational choices. Our findings reveal a significant negative relationship between course fees and attendance, demonstrating the importance of thoughtful pricing strategies in balancing revenue generation with accessible recreation. The research provides insights for policymakers and park administrators on optimizing public access while managing financial objectives. [ABSTRACT FROM AUTHOR]

The development of the times encourages the community to focus on the advancement of science and technology, to create optimal public services. Transportation is one of the sectors that is greatly affected by technological advances. The escalation of people's mobility demands has led to an increase in very significant means of transportation that are not proportional to the number of existing vehicles, resulting in congestion on the streets. Given the importance of the role of transportation, the government needs to provide mass transportation services that are by the needs of services that are regular, safe, comfortable, fast, precise, and affordable. This study uses a qualitative descriptive approach through observation, direct interviews, and documentation to collect data. The goal is to collect detailed information on improving the quality of DAMRI transportation services and analyze the factors that support and hinder these services. The results of the study show that the escalation of DAMRI service quality focuses on the Minimum Service Standards for Road-Based Mass Transportation, namely security, safety, comfort, affordability, equality, and order. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major problem in large cities worldwide. This project uses high-frequency data from the Los Angeles metropolitan area combined with an instrument that varies spatially and temporally to estimate the causal impact of an additional vehicle mile traveled on travel times. Specifically, the research team exploits the network structure of the Los Angeles highway system and uses crashes on close alternative routes as exogenous shocks to traffic demand. To do so, the team relies on Google Maps to determine the ideal route and alternatives for over 19,000 real-world commutes. The researchers estimate that at peak times an additional trip reduces speed by, on average, 0.22%. They find the optimal toll at peak times is 33 cents per mile, with the toll being lower, even zero, off-peak. The researchers show how this toll varies over space and time, as well as report on its distributional effects. This toll would more than double highway speeds during peak times and only requires reducing vehicle miles traveled (VMT) at the peak by 10%. The resulting social welfare gains are over two billion dollars per year.View the NCST Project Webpage

Curb parking is notoriously difficult to find in certain parts of New York City (NYC). NYC Department of Transportation (DOT) is considering implementing demand-responsive pricing for its passenger metered spaces to manage parking demand. This is a pricing mechanism whereby the price of curb parking fluctuates spatially and temporally in accordance with expected or actual demand. This report assesses successful permanent citywide implementations of demand-responsive curb pricing in San Francisco and Seattle to determine the most effective strategies for NYC DOT: these include using meter transaction data and historical occupancy data to estimate current parking occupancy and exploring possibilities of vehicle-mounted license plate recognition technology. To help make demand-responsive pricing more efficient and politically acceptable, the city can implement additional policies such a two-tier system for disability placards to target placard abuse or parking benefit districts to direct meter revenue toward public services on metered blocks. A data-driven demand-responsive curb pricing program that follows a set model for rate adjustments, in conjunction with technology and legislation changes to increase support for the program and reduce parking violations, as described in this report, can effectively increase parking availability in New York City and guide other city transportation agencies in their efforts to do the same.

Dynamic toll pricing based on demand can increase transportation revenue while also incentivizing travelers to avoid peak traffic periods. However, given the unpredictable nature of traffic, travelers lack the information necessary to accurately predict congestion, so dynamic pricing has minimal effect on demand. Dynamic toll pricing also poses equity concerns for those who lack other travel options. This research explores a potential remedy to these concerns by using a simple “futures market” pricing mechanism in which travelers can lock in a toll price for expected trips by prepaying for future tolls, with the future price increasing as more travelers book an overlapping time slot. This approach encourages travelers to avoid driving during the peak periods when pricing increases toward capacity or to purchase trips in advance when the price remains low or discounted, thus using infrastructure capacity more efficiently. Travelers that do not prepurchase their trip are subject to the real-time market price, which is determined by dynamic congestion pricing. This futures-market mechanism can augment existing toll collection technologies and provide travelers with sufficient pricing information and purchasing options to preplan their travel and avoid excessive prices.

Local folks have reported that roasting cashew nuts has fatal effects on domestic birds, especially chickens. This study compared the toxicological effect of acute (one-hour) inhalation of cashew nutshell fumes (CNSF) in Cockerel chickens and Wistar rats. Twenty-four mixed-sex Wistar rats were divided into four groups; control, rats exposed to 500ppm/hr., 1250ppm/hr. and 2500ppm/hr. of CNSF for one (1) hour. The chickens were also divided into three groups of 6 chickens each; control (unexposed chickens), chickens exposed to 500ppm/hr. and 1000ppm/hr. of CNSF for one (1) hour. Hematological analysis as well as histological examinations of the lungs and heart of the Chickens and Rats were carried out. After one hour of exposure, the toxicity signs noted include; scratching, twitching, irregular breathing, and tremors. All rats exposed to 2500ppm/hr of CNSF died with moderate pulmonary congestion. Analysis of hematological parameters in the surviving animals showed a significant (P<0.05) increase in hemoglobin (Hb%), packed cell volume (PCV%) and white blood cells (WBCx109L) counts in both chickens and Wistar rats exposed to CNSF compared with the unexposed control groups. No significant (P>0.05) differences were found between male and female rats for all the hematological parameters measured. The results also showed species-specific variations in all the hematological parameters. Histopathological evaluation reflects a mononuclear inflammatory infiltration of the striated heart muscles and the septa of alveoli in the lungs. These results further highlight the possible adverse health effects of exposure to cashew nut fumes in animals and humans. [ABSTRACT FROM AUTHOR]

Structural models of traffic congestion, such as the bottleneck model, are used to answer important, policy‐relevant questions. However, existing models typically assume that no travelers are inframarginal regarding when to travel; that is, given equilibrium travel times, no travelers strictly prefer their ex ante departure time to all others. In this article, I address this shortcoming by incorporating inframarginal travelers into these models. This change significantly improves these models' ability to fit the data and changes policy prescriptions. In the case of congestion pricing, it typically changes the optimal toll by at least 25% and significantly worsens the distributional impacts. [ABSTRACT FROM AUTHOR]

Urban road traffic is one of the primary sources of carbon emissions. Previous studies have demonstrated the close relationship between traffic flow characteristics and carbon emissions (CO2). However, the impact of dynamic traffic distribution on carbon emissions is rarely empirically studied on the network level. To fill this gap, this study proposes a dynamic network carbon emissions estimation method. The network-level traffic emissions are estimated by combining macroscopic emission models and recent advances in dynamic network traffic flow modeling, namely, Macroscopic Fundamental Diagram. The impact of traffic distribution and the penetration of battery electric vehicles on total network emissions are further investigated using the Monte Carlo method. The results indicate the substantial effect of network traffic distribution on carbon emissions. Using the urban expressway network in Ningbo as an example, in the scenario of 100% internal combustion engine vehicles, increasing the standard deviation of link-level traffic density from 0 to 15 veh/km-ln can result in an 8.9% network capacity drop and a 15.5% reduction in network carbon emissions. This effect can be moderated as the penetration rate of battery electric vehicles increases. Based on the empirical and simulating evidence, different expressway pollution management strategies can be implemented, such as petrol vehicle restrictions, ramp metering, congestion pricing, and perimeter control strategies. [ABSTRACT FROM AUTHOR]

In metro areas in the United States, those who cannot drive have limited access to job opportunities and services located in low-density suburbs that are poorly served by public transit; at the same time, congested metropolitan freeways cause significant travel delays for commuters who have no option but to drive long distances to job sites during peak periods. In this Viewpoint, I present a systemwide approach to financing and fulfilling the transportation needs of those who do not drive while at the same time providing a congestion-free travel choice for those who do. It combines congestion pricing with cash rewards for those who choose to share rides on a network of congestion-free lanes converted from general-use lanes to priced lanes. Net revenues support multimodal travel options including transit, carpooling, and mobility hubs with transportation services to and from trip origins and final destinations, also known as first- and last-mile services. The strategy would allow existing metropolitan freeway networks to be transformed into financially viable, sustainable, and equitable multimodal systems providing high-quality travel choices that could lead to more sustainable urban development patterns. [ABSTRACT FROM AUTHOR]

This survey proposes a new application of the inverse data envelopment analysis (InvDEA) in the problem of merging decision-making units (DMUs) to improve the performance of DMUs by removing congestion. Congestion is a factor in reducing production; therefore, removing it decreases costs and increases outputs. There are two significant subjects in the merging DMUs. Estimating the inherited inputs and outputs of a new production DMU with no congestion is the first problem while achieving a pre-specified efficiency level from the merged DMU is the second one. Both problems are examined using the ideas of inverse DEA and congestion. Using Pareto solutions to multiple-objective programming problems, sufficient conditions for inherited input/output estimates with no congestion and increasing efficiency are created. Besides, an example is perused for the reliability of the proposed approach in basic research institutes in the Chinese Academy of Science (CAS) in 2010. [ABSTRACT FROM AUTHOR]

Developing country megacities suffer from severe road traffic congestion, yet the level of congestion is not a direct measure of equilibrium inefficiency. I study the peak‐hour traffic congestion equilibrium in Bangalore. To measure travel preferences, I use a model of departure time choice to design a field experiment with congestion pricing policies and implement it using precise GPS data. Commuter responses in the experiment reveal moderate schedule inflexibility and a high value of time. I then show that in Bangalore, traffic density has a moderate and linear impact on travel delay. My policy simulations with endogenous congestion indicate that optimal congestion charges would lead to a small reduction in travel times, and small commuter welfare gains. This result is driven primarily by the shape of the congestion externality. Overall, these results suggest limited commuter welfare benefits from peak‐spreading traffic policies in cities like Bangalore. [ABSTRACT FROM AUTHOR]

Copyright of Uluslararasi Ekonomi ve Yenilik Dergisi is the property of Karadeniz Technical University, Depertmant of Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Option pricing is crucial in enabling investors to hedge against risks. The Black-Scholes option pricing model is widely used for this purpose. This paper investigates whether the Black-Scholes model is a good indicator of option pricing in the United States stock market. We examine the relevance of the Black-Scholes model to certain stocks using paired sample t-test and Corrado and Miller's approximation for the implied volatility. Empirical tests are applied to determine the significance of the relationship between the actual market values and the Black-Scholes model values. Paired sample t-tests are applied to 582 call options and 579 put options. The empirical test results show that there is no significant difference between the actual market premium value and the Black-Scholes model premium value for seven out of nine stocks considered for call options, and four out of nine stocks considered for put options. Thus, we conclude that the Black-Scholes option pricing model can be used to price call options but is not suitable for pricing put options in the United States stock market. [ABSTRACT FROM AUTHOR]

In the era of the rapid development of e-commerce, many retailers choose to launch promotional activities to become consumers' first choice for shopping. Since price discounts can greatly attract consumers, the e-commerce platforms have also begun to implement discount pricing. It is urgent for e-commerce platforms and retailers to formulate reasonable discount strategies to achieve sustainable business. In this paper, we construct a dynamic game model for implementing discount pricing on an e-commerce platform and two retailers, we study the market equilibrium between the two retailers and the e-commerce platform under various scenarios that considering consumers' strategic waiting behavior and competition between the two retailers, we further discuss the effectiveness of retailer discount pricing and the double discount pricing of the platform and retailers. We show that the optimal pricing decreases as the difference in product quality narrows under both pricing strategies. Low-quality retailers implementing a double discount pricing strategy are in relatively higher demand only when the difference in product quality is small. High-quality retailers implementing the retailer discount pricing strategy are in relatively higher demand only when the product quality difference is large. Double discount pricing is desirable for both e-commerce platforms and retailers and can be used to effectively achieve Pareto improvement in the market by increasing their expected profit. Our results emphasize the role of product quality and the value of the double discount pricing strategy. The double discount pricing strategy weakens the profit advantage that retailers and platforms gain from it as the rebate intensity and rebate redemption rates increase. [ABSTRACT FROM AUTHOR]

This paper studies the sharing mode selection and optimal pricing for an electric vehicle manufacturer (EVM). We evaluate three sharing modes, i.e. Mode N, car sales business only, Mode S, car sales business and car-sharing service provided by a self-built sharing platform, and Mode T, car sales business and car-sharing service provided by a third-party sharing platform. Further, we consider two types of government subsidy policies: purchase and production subsidies. We find that for purchase subsidies, the EVM should select Mode S if the transaction cost per sharing service is low, and select Mode T otherwise; for production subsidies, the EVM should select Mode S. Regardless of the subsidy policy, the optimal sales prices of EVs under Mode N and Mode S are the same, but both are lower than that under Mode T. The optimal rental price of EVs under Mode S is lower than that under Mode T. [ABSTRACT FROM AUTHOR]

In the planning stage of a BOT highway project, the future traffic demand plays a pivotal role on designing contract factors. Considering that the traffic demand generally has variations with time, the constant toll during the concession period obviously is not a good choice. And the different toll charges classified by vehicles types definitely affect the traffic composition and maintenance cost. To address this practical issue, this study proposes the mixed integer nonlinear programming models to select the period-dependent toll charges for multi-type vehicles and highway capacity, by considering both the objectives of the government and private firms. Then two solution algorithms are provided to solve our proposed models. Finally, numerical experiments are conducted to assess the applicability and efficiency of our models and algorithms. • Based on an empirical formula, pavement rehabilitation costs are estimated by the time-varying traffic demand. • Mixed integer nonlinear programming models are proposed to select the optimal highway capacity and toll charges by vehicle type. • Two solution algorithms are provided to solve our proposed model. • Numerical experiments are conducted to assess our proposed methodology. [ABSTRACT FROM AUTHOR]

Public support is crucial for the successful implementation of congestion charging schemes. The Tradable Credits Scheme (TCS), a novel concept built upon the cap-and-trade principle, is believed to possess qualities of effectiveness and fairness that should lead to greater public approval. This study investigates the level of acceptance of TCS among the public and its impact on the propensity to shift travel modes in the TCS framework. In this study, we used the Theory of Planned Behavior, encompassing components such as attitudes toward TCS, subjective norms and perceived behavioral control and assessed the perceived effectiveness and equity of TCS. Data were acquired through an online survey involving 544 participants in China. The findings indicate that public acceptance of TCS is significantly influenced by subjective norms and attitudes toward TCS. In turn, behavioral intention is indirectly shaped by these factors, with acceptance serving as a mediator. Perceived behavioral control exerts a direct influence on behavioral intention, while perceived effectiveness and equity positively impact both the acceptance of TCS and behavioral intention. Moreover, latent profile analysis was used to determine the influence of various factors on TCS acceptance, following which we categorized the participants into four distinct groups. The results show that age, city scale, income, holding a driving license or not, car ownership, necessity through congested areas, main transportation modes, and the frequency of using different travel modes all affect the distribution of individuals in support or rejection groups Notably, over one-third of participants express TCS acceptance, with most holding a neutral stance, indicative of TCS's potential for public acceptance with effective guidance and comprehensive explanation. In summary, this research provides novel insights into the multifaceted factors that influence individuals' acceptance of TCS and offers valuable assistance in promoting an innovative traffic demand management policy. • The public's acceptance of TCS and behavioral intention is explored through the Theory of Planned Behavior; • An online survey is conducted in China through a questionnaire platform; • The effect of various personal factors is examined via Latent profile analysis; • Promoting the perceived effectiveness and perception of equity are effective. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the fast-charging demand of electric vehicles, causing feeder load and voltage imbalances during operation, this paper introduces a spatio-temporal pricing strategy tailored to enhance feeder operation equilibrium. This approach facilitates the spatio-temporal guidance of fast-charging loads for electric vehicles in operation. This paper begins by formulating a spatio-temporal distribution model for electric vehicle fast-charging loads, considering owners' preferences. It further develops a behavioral model for the travel choices of electric vehicles, illustrating the impact of spatio-temporal electricity pricing at fast-charging stations on load distribution. Next, it proposes a multi-objective spatio-temporal pricing model and its solution method specifically designed for feeder-balance-oriented fast-charging stations. This model targets the minimization of the spatio-temporal imbalance in feeder voltage and load. It takes a comprehensive approach, considering the constraints of the spatio-temporal load distribution model and optimal power flow model. The resulting spatio-temporal pricing model for fast-charging stations is effectively solved using the extended Pareto evolutionary algorithm. To validate the effectiveness of the proposed method in achieving feeder balancing, this paper analyzes two examples: a self-built 29- node road network and a 9-node distribution network, as well as a 66-node road network and a 33-node distribution network in the Xinjiang region. The results show that the proposed method can effectively guide the charging of electric vehicles and make the load distribution more balanced. [ABSTRACT FROM AUTHOR]

This paper devotes its attention to the increasingly advanced development of machine learning and the possibility of applying such learning in the field of road transportation. Sales of road vehicles form a significant part of a country's economy and the work of pricing them has a major impact on the purchase or sale. Used vehicles are still the first choice for a large part of the population, as the purchase of a new vehicle results in an immediate drop in price. Determining the right price for a used car is crucial for a proper purchase and a quick sale. Some vehicle parameters affect the price of a vehicle more and some less. This paper highlights the need for selection of such parameters and describes the development of a system for determining the price of vehicles based on these parameters. The system has the potential to serve as a determinant of whether the purchase or sale of a vehicle is advantageous in terms of vehicle price. Also, such a system serves as a predictor of price development, which helps to regulate the economy to ensure market sustainability, proper economics and the possibility of early action if necessary. [ABSTRACT FROM AUTHOR]

The number of vehicles is increasing rapidly, but this is different from the rise in the number and length of roads, so there is often a density of vehicles on the road, especially on toll roads during peak hours. The objective of this research is to examine the application of dynamic toll tariffs in reducing toll road congestion. This research used variables that determine the amount and timing of dynamic tariffs, including the level of toll road density, Ability to Pay (ATP), Willingness to Pay (WTP), and tariff sensitivity to traffic volume. The result shows that applying dynamic tariffs on toll roads can change users' patterns and travel time. In addition, this dynamic tariff can break down the density of toll road during peak hours. [ABSTRACT FROM AUTHOR]

The paper develops a theoretic equilibrium model for ridesplitting markets with specific considerations of origin-destination demand patterns, competition with other transport modes, characteristics of en route matching, and spatial allocation of ridesplitting vehicles, to adequately portray the intertwined relationships between the endogenous variables and decisions. The operation property of the market under distance-based unified pricing is analyzed through the response of system performance indicators to the decisions. Moreover, a gradient descent algorithm is derived to find optimal operating strategies in the monopoly scenario and social optimum scenario. Leveraging the tight connection between trip's utility and level of service (LoS), the paper then proposes a utility-based compensation pricing method to alleviate the inequity issue in ridesplitting, which results from the variance in waiting time and detour time and the implementation of unified pricing. Specifically, the trip fare of those with an initial utility smaller than a threshold will be compensated following a predefined compensation function. We compare its effectiveness and influence in different scenarios through numerical experiments at Munich. The results show that the proposed pricing method can improve the LoS and equity without losing any profit and welfare, and can even achieve increments in maximum profit and social welfare under certain conditions. [ABSTRACT FROM AUTHOR]

Congestion pricing and License Plate Rationing (LPR) are classical transportation policies targeting travel demand. Travelers are constrained to reduce their cars' usage to improve traffic conditions or decrease exhaust gases emissions. They have already been proven effective in the field but have also received some criticism for being unfair or badly perceived by users. On the other side, Tradable Credit and Permit Schemes (TCS/TPS) have been investigated in the literature for more than a decade but have never been implemented in practice. In this paper, we present a large-scale dynamic simulation study, corresponding to the morning peak hour for the Lyon city (France) to benchmark pricing, LPR, TCS, and TPS in terms of modal shift from personal cars to Public Transportation (PT), over a horizon of several days and with different charging schemes. Congestion dynamic in the transportation system is reproduced using a trip-based Macroscopic Fundamental Diagram (MFD) framework. We compute the modal assignment at equilibrium by iteratively solving a Quadratic Problem (QP). The initial formulation, presented in previous work for a single day and a uniform TCS, is extended to account for different days, validity cycles, charging schemes, and TPS. The benchmarking shows that the TCS outperforms LPR in terms of both social cost and carbon emissions. Making credit valid over multiple days reduces the price variability while keeping the same system performances and gain distributions. [ABSTRACT FROM AUTHOR]

This study investigates the impact of economic incentives on travel-related physical activity, leveraging the London Congestion Charge's disincentivising of sedentary travel modes via increasing the cost of private car use within Central London. The scheme imposes charges on most types of cars entering, exiting, and operating within the Central London area, while individuals living inside the charging zone are eligible for a 90% reduction in congestion charges. Geographical location information provides the full-digit postcode data necessary to precisely identify the eligibility for the discount of participants in the London Travel Demand Survey for the period 2005–2011. Using a boundary regression-discontinuity design reveals a statistically significantly positive impact on active commuting (i.e. cycling and walking) around the border of the charging zone. The effect is larger for lower-income households and car owners. The findings are robust against multiple specifications and validation tests. [ABSTRACT FROM AUTHOR]

The electrification of public transportation is one significant step towards the sustainability of modern society. Both power networks and traffic networks are influenced by the optimal allocation of electric‐bus (e‐bus) charging stations. In this paper, the optimal operation cost of e‐bus charging stations is first designed based on the charging price and congestion toll. Charging price and congestion toll are used to guide charging behavior to reduce total traveling and waiting time. Second, the e‐bus charging station allocation method is proposed. The planning costs are optimized to locate e‐bus charging stations on coupled power and traffic networks. Third, a non‐dominated sorting genetic algorithm‐II (NSGA‐II) is adopted to solve the multi‐objective optimization problem, which maximizes the captured traffic flow and minimizes infrastructure investment at the same time. The effectiveness of NSGA‐II is proved compared with the enumeration method. Finally, based on a 33‐node power distribution network and a 77‐node traffic network, simulation results demonstrate the feasibility of the e‐bus charging station allocation method. [ABSTRACT FROM AUTHOR]

Congestion pricing improves economic efficiency, but it may lead to inequitable outcomes. A key policy priority in California is identifying ways to avoid the hardship of congestion pricing on low income or other vulnerable populations. This study uses data from a congestion pricing experiment in the Seattle metro area to examine the feasibility of using revenue from congestion pricing to compensate those harmed by the policy. Results indicate that the initial burden of congestion pricing is highly inequitable, with the lowest income drivers paying an average of 7 percent of their weekly income in congestion charges. There are also considerable differences in burdens within income groups. We show that policymakers face a tradeoff in ameliorating these two types of unequal burdens. Returning an equal fraction of the toll revenue to all drivers can make a policy progressive on average, but doing so leaves many drivers either overcompensated or under-compensated. We then show that while compensation packages based on basic demographic information could improve targeting, many low-income drivers would be left with large proportional burdens because of the fundamental difficulty in predicting individual-level tax burdens. Survey data on travel behavior from Seattle and California metro areas show that the difficulty of designing equitable transfers would be similar in the California metro areas most likely to consider adopting some form of congestion pricing.

Congestion pricing (CP) is widely considered to have significant potential for effectively reducing vehicle miles traveled, reducing emissions, and providing a reliable revenue source for transportation investments. This study evaluated cities interested in CP—five in the U.S. (Boston, Los Angeles, New York, San Francisco, Seattle) and two in other countries (Vancouver, Canada, and Auckland, New Zealand). This study examines the following features of a CP system for each of these cities: 1) duration of CP investigations, 2) equity mitigations, 3) range of alternatives considered, 4) public engagement, and 5) importance of emissions reductions. Timelines are impossible to predict with certainty, but New York and Auckland appear closest to implementation. Vancouver, San Francisco, and Seattle are well into the process; and Boston and Los Angeles are early in the process. Other key findings include that most of the cities start considering a range of options before narrowing down to comparing more detailed CP systems. Vancouver and San Francisco have made public engagement a cornerstone of their plan development, using polls and workshops to finetune the details of their CP proposals. In contrast, Auckland, while still engaging with stakeholders and experts for guidance, has mainly focused on how to ensure public support and understanding of the proposals they recommend. In terms of equity, discounts are a common and primary strategy proposed among the cities, but some also develop a more comprehensive set of equity policies to accompany a CP system.

Pricing and traffic rationing have become popular and economically viable ways to reduce traffic congestion in major cities' central business districts (CBDs). Time-based and duration-based pricing rules affect travel behavior in Tehran, Iran's capital. To figure out the consequences, 1388 congestion pricing zone and 983 odd-even traffic rationing zone travelers were surveyed in 2018–2019. The stated preference survey and error component logit model modeled trip variations in modal shift, route choice, and time of travel in a day. A generalized mixed logit model examined mode choice behavior using revealed and stated preferences. The error component logit model suggests that the duration-based scenario will lead to a modal shift, trip alteration, and trip cancellation, whereas the time-based scenario will change the time or destination. A generalized mixed logit model and revealed and stated preference data imply that duration-based pricing is more successful than time-based pricing in shifting private vehicle trips to other modes. Also, results show public transit is the most common demand deviation, and the time-based scenario is more successful than the duration-based scenario. The mode shift to Snap is lower than other transport modes in both scenarios, suggesting that on-demand ride-hailing is a less vital competitor in zones.

Can congestion pricing be implemented in a way that protects vulnerable residents of California? This report examines that question from two perspectives. First, we empirically estimate the size of the vulnerable population likely to be impacted if congestion pricing were introduced on California’s urban freeways. Our estimates suggest that 13% of households, as a result of their low incomes and current travel habits, might be unduly burdened by a freeway tolling program in California. Second, we consider ways to mitigate these burdens. In particular, we compare freeway use to use of other metered network infrastructure, like electricity grids or water systems. We suggest that assistance programs from these utilities provide a useful model for protecting low-income drivers from road prices, and further note that policymakers would be less constrained in progressively redistributing congestion toll revenue than they would be in redistributing utility revenue.