Optimal Pricing – Investigating the use of simulated dynamic pricing to optimize transport systems

Users of transport systems do not only accept generalized costs for themselves (e.g. own travel time), but they also cause damages to other transport users (e.g. congestion, accidents), to residents (e.g. noise, carcinogenic air pollutants) and to the global population (greenhouse gas emissions). In this project, a simple mechanistic approach is proposed to to feed the costs of these damages back to the persons causing them. A model is developed which calculates dynamic and user-specific correction terms, to be added to each user's generalized travel cost. This cost correction may be interpreted as a price to be charged from the transport user, but it can as well just be interpreted as a computational device to move the simulated system to a better operating point. This approach will be integrated into an open source multi-agent simulation framework in which each transport user is modeled microscopically. The model will be used to investigate how the simulated dynamic prices can be used to optimize transport systems. Thereby, the proposed model makes use of the innovative agent-based simulation methodology which particularly allows for heterogeneity in demand, large-scale networks, dynamic congestion and complex behavioral decisions, such as mode choice, route choice and departure time choice. In order to demonstrate the real world applicability, the proposed optimization approach is applied to the Greater Berlin area.

DFG: German Research Foundation

Prof. Dr. Kai Nagel, TU Berlin

A movie about the project Optimal Pricing can be found here.

I. Kaddoura and A. Agarwal and B. Kickhöfer (2017). Simulation-based optimization of congestion, noise and air pollution costs: the impact of transport users' choice dimensions.  ITEA Annual Conference and School on Transportation Economics

José R. Correa and Tobias Harks and Kai Nagel and Britta Peis and Martin Skutella (2016). Dynamic Traffic Models in Transportation Science (Dagstuhl Seminar 15412). Dagstuhl Reports. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 19–34.

Kaddoura, I. (2015). Marginal Congestion Cost Pricing in a Multi-Agent Simulation: Investigation of the Greater Berlin Area.  Journal of Transport Economics and Policy, 560–578.

Kaddoura, I. AND Bischoff, J. AND Nagel, K. (2018). Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles. 

Ihab Kaddoura and Joschka Bischoff and Kai Nagel (2020). Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles.  Transportation Research Part A: Policy and Practice, 48–63.

Kaddoura, I. and Kickhöfer, B. (2014). Optimal Road Pricing: Towards an Agent-based Marginal Social Cost Approach. 

Kaddoura, I. and Kröger, L. and Nagel, K. (2017). An activity-based and dynamic approach to calculate road traffic noise damages.  Transportation Research Part D: Transport and Environment, 335–347.

Kaddoura, I. and Kröger, L. and Nagel, K. (2017). User-specific and dynamic internalization of road traffic noise exposures.  Networks and Spatial Economics, 153–172.

Kaddoura, I. and Nagel, K. (2016). Activity-based computation of marginal noise exposure costs: Implications for traffic management.  Transportation Research Record

Kaddoura, I. and Nagel, K. (2016). Agent-based congestion pricing and transport routing with heterogeneous values of travel time savings.  Procedia Computer Science, 908–913.

Ihab Kaddoura and Kai Nagel (2016). Simultaneous optimization of traffic congestion and noise exposures. 

Kaddoura, I. and Nagel, K. (2018). Simultaneous internalization of traffic congestion and noise exposure costs.  Transportation, 1579–1600.

Kaddoura, I. and Nagel, K. (2019). Congestion pricing in a real-world oriented agent-based simulation context.  Research in Transportation Economics, 40–51.

Kickhöfer, B. and Kaddoura, I. and Neumann, A. and Tirachini, A. (2012). Optimal public transport supply in an agent-based model: The influence of departure time choice on operator's profit and social welfare.  Kuhmo Nectar Conference on Transportation Economics

Kühnel, N. and Kaddoura, I. and Möckel (2019). Incorporation of noise shielding in an agent-based transport model by using volunteered geographic data.  Procedia Computer Science, 808–813.