Congestion tolling — Dollars versus tokens: Within-day dynamics

Seshadri, Ravi; Palma, André de; Ben‐Akiva, Moshe

doi:10.1016/j.trc.2022.103836

Cited by 9 publications

(4 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second captures decisions related to occasional cargo needs on longer trips. Denmark, Finland, Iceland, Norway, and Sweden, [38] Mixed logistic and supply model for static congestion.…”

Section: Literature Reviewmentioning

confidence: 99%

Reducing Urban Traffic Congestion via Charging Price

2023

View full text Add to dashboard Cite

Traffic jams are one of the major transportation problems. The United States spends USD billions to mitigate the problem, and not always with good outcomes. This problem increases and has effects on sustainable transport, such as life quality, pollution, perishables, and costs. Large cities reduce traffic jams through congestion charges. This paper aims to reduce urban traffic congestion by estimating the charge through a multivariable model. It studies the main jammed areas in Santiago, Chile. The data came from published surveys. The model evaluation included Fisher multiple regression (F) and the determination coefficient (R²). These validations showed that the model is statistically significant. They also showed that the parameter estimation was good. Finally, this model contributes to improving the Sustainable Development Goals, such as SDG 3, SDG 11, and SDG 13, which may be successfully applied to Santiago City, as well as to any city worldwide.

show abstract

“…The second captures decisions related to occasional cargo needs on longer trips. Denmark, Finland, Iceland, Norway, and Sweden, [38] Mixed logistic and supply model for static congestion.…”

Section: Literature Reviewmentioning

confidence: 99%

Reducing Urban Traffic Congestion via Charging Price

2023

View full text Add to dashboard Cite

show abstract

“…Nevertheless, other modeling approaches have also been developed, e.g., an MFD-based approach in a multimodal context (Balzer and Leclercq 2022) or agent-based modeling approaches (Tian and Chiu 2015), where also the aspect of multi-period budgets has been included by (e.g., Miralinaghi and Peeta 2016). Further related to multi-period budgets are the within-day dynamics of a TMC scheme, which has been studied, e.g., by Seshadri et al (2022), and the impact of day-to-day variability in demand and supply on the performance of a TMC scheme, which has been studied, e.g., by Lindsey et al (2023). Research also studied user perceptions, the system's acceptance, and feasibility (Krabbenborg et al 2020(Krabbenborg et al , 2021Kockelman and Kalmanje 2005).…”

Section: Introductionmentioning

confidence: 99%

Modeling MobilityCoins—Charges, Incentives and Multi-period Budgets in Multimodal Transportation Networks

Loder,

Bogenberger

2024

Data Sci. Transp.

View full text Add to dashboard Cite

MobilityCoins are a tradable mobility credit (TMC) scheme variant. TMC schemes are a cap-and-trade scheme for managing mobility that are designed to limit negative externalities, e.g., congestion, of traffic. Next to having link-specific or origin–destination-specific charges for cars as in the common TMC scheme, the MobilityCoin scheme’s distinctive elements are accommodating link-specific and origin-and-destination-specific charges and incentives for all modes of transport as well as being considered a mobility currency that can be earned, saved, and spent in multiple time periods. These distinctive features of the MobilityCoin scheme does not alter the core behavioral mechanism of TMC schemes of increasing car travel costs, but these features interfere with the credit market in terms of market volume and market price that ultimately affects traffic outcomes, e.g., an uncontrolled market volume increase can lower the market price that in turns increases the attractiveness of using the car. In this paper, we develop a mathematical model of multimodal macroscopic network flows and a MobilityCoin market to investigate the impacts of charges, incentives, and multi-period budgets. The model is implemented as a single-day model with an integration of sensitivity for multi-period budgets to study how the outcomes in the transportation system change with charges, incentives, and multi-period budgets. Further, we discuss implications for the policy design of MobilityCoins schemes.

show abstract

“…By virtue of the popularization of wireless communication technologies and mobile devices, mobile crowd sensing (MCS) becomes a typical sensing paradigm for data collection [1], traffic flow monitoring [2], and advanced traffic management system [3]. Based on the fine-grained traffic data collected via MCS and GPS technologies, some proactive traffic control policies can be designed to optimize the traffic system efficiency, such as variable speed limit (VSL) control method [4], route guidance system [5], traffic congestion pricing [6], and so on. In a road traffic network, travelers' selfish routing behaviors may result in a significant efficiency loss for the traffic network, which in turn worsens traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

“…To quantitatively analyze the effects of TCS on managing network mobility, [11] established a user equilibrium traffic assignment model under a given TCS, and explored the SO TCS design problem of which the optimization objective is to minimize the TTT of all travelers. This work was then widely extended by considering some more realistic factors in specific application scenarios, such as different market trading mechanisms with transaction costs [9,12], multiclass users with heterogeneous value of time (VOT) [10,13], mixed equilibrium behaviors [14], travelers' loss aversion behaviors [15], uncertainty of traffic network [16,17], along with network design problem under TCS [18], maximizing network reserve capacity [19,20], managing bottleneck congestion [21][22][23][24], dynamic traffic control based on macroscopic fundamental diagram (MFD) [25,26], designing implementation policies of TCS [6,27,28], and so on. For more studies on TCS, readers are referred to the latest review paper [29].…”

Section: Introductionmentioning

confidence: 99%

Modeling Impacts of Implementation Policies of Tradable Credit Schemes on Traffic Congestion in the Context of Traveler’s Cognitive Illusion

et al. 2023

View full text Add to dashboard Cite

A tradable credit scheme (TCS) is a novel traffic demand management (TDM) measure that can effectively mitigate traffic congestion in a revenue-neutral way. Under a given TCS, the cognitive illusion (CI) would occur when travelers instinctively use a specious thinking logic to estimate travel cost. The traveler’s CI would significantly influence his/her route choice behaviors, and thus the regulation effect of TCS on mitigating traffic congestion. To reveal the impacts of implementation policies of TCS on managing network mobility in the context of the traveler’s CI, this study investigated the traffic equilibrium assignment model with consideration of the traveler’s CI and the specific implementation policies of TCS. By incorporating the two types of factors into the generalized path travel cost (GPTC), the coupled user equilibrium (UE) and market equilibrium (ME) conditions are established to describe the equilibrium state of the traffic network under a given TCS. As the implementation policies of TCS are factored in the GPTC, different types of initial credit distribution scheme (ICDS) and the transaction costs (TC) of trading credits can be analyzed within the unified model framework. The coupled UE and ME conditions are then reformulated as an equivalent variational inequality (VI) model, and the sufficient conditions for the uniqueness of UE link flows and ME credit price are also provided. The system optimal (SO) TCS design problem is further investigated to achieve the minimum total travel time (TTT) of the transportation network, and two analytical methods are proposed to obtain the SO TCS in the context of the traveler’s CI. Numerical experiments are presented to verify the proposed model and methods. The results show that the presence of the traveler’s CI has an effect of lowering the ME credit price, and ICDS and TC have a complex network-wide influence on the ME credit price and UE link flows, which depends on the specific values of the relevant parameters.

show abstract

Congestion tolling — Dollars versus tokens: Within-day dynamics

Cited by 9 publications

References 56 publications

Reducing Urban Traffic Congestion via Charging Price

Reducing Urban Traffic Congestion via Charging Price

Modeling MobilityCoins—Charges, Incentives and Multi-period Budgets in Multimodal Transportation Networks

Modeling Impacts of Implementation Policies of Tradable Credit Schemes on Traffic Congestion in the Context of Traveler’s Cognitive Illusion

Contact Info

Product

Resources

About