An Optimal Congestion and Cost-Sharing Pricing Scheme for Multiclass Services

We study in this paper a social welfare optimal congestion-pricing scheme for multiclass queuing services which can be applied to telecommunication networks. Most of the literature has focused on the marginal price. Unfortunately, it does not share the total cost among the different classes. We investigate here an optimal Aumann-Shapley congestion-price which verifies this property. We extend the work on the Aumann-Shapley price for priority services, based on the results on the marginal price: instead of just determining the cost repartition among classes for rates, we obtain the rates and charges that optimize the social welfare

Advanced pricing and rationing policies for large scale multimodal networks

The applying of simplified schemes, such as cordon pricing, as second-best solution to the toll network design problem is investigated here in the context of multiclass traffic assignment on multimodal networks. To this end a suitable equilibrium model has been developed, together with an efficient algorithm capable of simulating large scale networks in quite reasonable computer time. This model implements the theoretical framework proposed in a previous work on the toll optimization problem, where the validity of marginal cost pricing for the context at hand is stated. Application of the model to the real case of Rome shows us, not only that on multimodal networks a relevant share (up to 20%) of the maximum improvements in terms of social welfare achievable with marginal cost pricing can in fact be obtained through cordon pricing, but also that in practical terms rationing is a valid alternative to pricing, thus getting around some of the relevant questions (theoretical, technical, social) the latter raises. As a result we propose a practical method to analyze advanced pricing and rationing policies differentiated for user categories, which enables us to compare alternative operative solutions with an upper bound on social welfare based on a solid theoretical background. (c) 2005 Elsevier Ltd. All rights reserved

From urban congestion pricing to tradable mobility credits: a review

Congestion is still a big challenge for urban mobility while vehicle sharing, eCommerce and autonomous vehicles will likely increase the unit veh-km of each vehicle and the density of vehicles moving on the streets. Urban vehicle congestion pricing schemes have been taken as effective solutions to this problem. This paper first reviews the research and application cases of urban congestion pricing through recent years, although with the well-developed theoretical basis and successful practices in Singapore, London, Stockholm, Milan, etc., public acceptance and equity concerns are still the main issues for such policies’ implementation. To circumvent the shortcomings of congestion pricing, a scheme of tradable mobility credits is proposed as an alternative. As travellers are distributed mobility credits within a specific urban area, which are allowed to be traded, those with low vehicle-using demands can sell their credits to those with more demands. Therefore with this scheme, people have the initiative to reduce the use of vehicles. This paper reviews the studies on this new urban mobility management strategy and compared it with ordinary congestion pricing schemes. Finally, we conclude the gap and possible directions for future works.Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles::11.2 - Per a 2030, proporcionar accés a sistemes de transport segurs, assequibles, accessi­bles i sostenibles per a totes les persones, i millorar la seguretat viària, en particular mitjan­çant l’ampliació del transport públic, amb especial atenció a les necessitats de les persones en situació vulnerable, dones, nenes, nens, persones amb discapacitat i persones gransPostprint (published version

A new approach to service provisioning in ATM networks

The authors formulate and solve a problem of allocating resources among competing services differentiated by user traffic characteristics and maximum end-to-end delay. The solution leads to an alternative approach to service provisioning in an ATM network, in which the network offers directly for rent its bandwidth and buffers and users purchase freely resources to meet their desired quality. Users make their decisions based on their own traffic parameters and delay requirements and the network sets prices for those resources. The procedure is iterative in that the network periodically adjusts prices based on monitored user demand, and is decentralized in that only local information is needed for individual users to determine resource requests. The authors derive the network's adjustment scheme and the users' decision rule and establish their optimality. Since the approach does not require the network to know user traffic and delay parameters, it does not require traffic policing on the part of the network

Modelling mixed autonomy traffic networks with pricing and routing control

Connected and automated vehicles (CAVs) are expected to change the way people travel in cities. Before human-driven vehicles (HVs) are completely phased out, the urban traffic flow will be heterogeneous of HVs, CAVs, and public transport vehicles commonly known as mixed autonomy. Mixed autonomy networks are likely to be made up of different route choice behaviours compared with conventional networks with HVs only. While HVs are expected to continue taking individually and selfishly selected shortest paths following user equilibrium (UE), a set of centrally controlled AVs could potentially follow the system optimal (SO) routing behaviour to reduce the selfish and inefficient behaviour of UE-seeking HVs. In this dissertation, a mixed equilibrium simulation-based dynamic traffic assignment (SBDTA) model is developed in which two classes of vehicles with different routing behaviours (UE-seeking HVs and SO-seeking AVs) are present in the network. The dissertation proposes a joint routing and incentive-based congestion pricing scheme in which SO-seeking CAVs are exempt from the toll while UE-seeking HVs have their usual shortest-path routing decisions are subject to a spatially differentiated congestion charge. This control strategy could potentially boost market penetration rate of CAVs while encouraging them to adopt SO routing behaviour and discouraging UE-seeking users from entering congested areas. The dissertation also proposes a distance-based time-dependent optimal ratio control scheme (TORCS) in which an optimal ratio of CAVs is identified and selected to seek SO routing. The objective of the control scheme is to achieve a reasonable compromise between the system efficiency (i.e., total travel time savings) and the control cost that is proportional to the total distance travelled by SO-seeking AVs. The proposed modelling frameworks are then extended to bi-modal networks considering three competing modes (bus, SO-seeking CAVs, and UE-seeking HVs). A nested logit-based mode choice model is applied to capture travellers’ preferences toward three available modes and elasticity in travel demand. A dynamic transit assignment model is also deployed and integrated into the mixed equilibrium SBDTA model to generate equilibrium traffic flow under different scenarios. The applicability and performance of the proposed models are demonstrated on a real large-scale network of Melbourne, Australia. The research outcomes are expected to improve the performance of mixed autonomy traffic networks with optimal pricing and routing control

Equitable Dynamic Pricing for Express Lanes

Express lanes mitigate traffic congestion by providing a time-reliable alternative and exploiting travelers\u27 willingness to pay to generate revenue for infrastructure projects. Over the last decade, equity and fairness issues for express lanes have been considered; however, there is a lack of guidance on the design of equitable discounts. In this article, we present a modeling framework for the analysis of equity issues with express lanes for tolls optimized for different objectives. Through simulation-based optimization of tolls using reinforcement learning, we show that the choice of dynamic tolls impacts the delay differentials across different groups. We find that higher toll values and higher demand worsen the delay differentials across travel groups. We also prove that discounts proportional to travelers\u27 value of time address delay differentials across the travel groups, where the optimal discounts may be a function of current toll and travel time savings. The analysis of tradeoffs between equity, revenue, and delay reveals that equitable discounts may result in up to 34% loss of revenue and up to 9% increase in delay. Research findings suggest (a) designing discounts proportional to VOT which can be correlated with income groups and (b) balancing the tradeoffs by carefully identifying the agency\u27s priorities