Heterogeneous Tolls and Values of Time in Multi-agent Transport Simulation

In evolutionary algorithms, agents' genotypes are often generated by more or less random mutation, followed by selection based on the fitness of their phenotypes. This paper shows that elements of this principle can be applied in multi-agent transport simulations, in the sense that a router, when faced with complex interactions between heterogeneous toll levels and heterogeneous values of time, can resort to some amount of randomness rather than being able to compute the exact best solution in every situation. The computational illustrations are based on a real world case study in the province of Gauteng, South Africa

The correlation of externalities in marginal cost pricing: lessons learned from a real-world case study

Negative externalities cause inefficiencies in the allocation of capacities and resources in a transport system. Marginal social cost pricing allows to correct for these inefficiencies in a simulation environment and to derive real-world policy recommendations. In this context, it has been shown for analytical models considering more than one externality, that the correlation between the externalities needs to be taken into account. Typically, in order to avoid overpricing, this is performed by introducing correction factors which capture the correlation effect. However, the correlation structure between, say, emission and congestion externalities changes for every congested facility over time of day. This makes it close to impossible to calculate the factors analytically for large-scale systems. Hence, this paper presents a simulation-based approach to calculate and internalize the correct dynamic price levels for both externalities simultaneously. For a real-world case study, it is shown that the iterative calculation of prices based on cost estimates from the literature allows to identify the amplitude of the correlation between the two externalities under consideration: for the urban travelers of the case study, emission toll levels—without pricing congestion—turn out to be 4.0% too high in peak hours and 2.8% too high in off-peak hours. In contrary, congestion toll levels—without pricing emissions—are overestimated by 3.0% in peak hours and by 7.2% in off-peak hours. With a joint pricing policy of both externalities, the paper shows that the approach is capable to determine the amplitude of the necessary correction factors for large-scale systems. It also provides the corrected average toll levels per vehicle kilometer for peak and off-peak hours for the case study under consideration: again, for urban travelers, the correct price level for emission and congestion externalities amounts approximately to 38 EURct/km in peak hours and to 30 EURct/km in off-peak hours. These toll levels can be used to derive real-world pricing schemes. Finally, the economic assessment indicators for the joint pricing policy provided in the paper allow to compare other policies to this benchmark state of the transport system

Agent-Based Model of Price Competition and Product Differentiation on Congested Networks

Using consistent agent-based techniques, this research models the decision-making processes of users and infrastructure owner/operators to explore the welfare consequence of price competition, capacity choice, and product differentiation on congested transportation networks. Component models include: (1) An agent-based travel demand model wherein each traveler has learning capabilities and unique characteristics (e.g. value of time); (2) Econometric facility provision cost models; and (3) Representations of road authorities making pricing and capacity decisions. Different from small-network equilibrium models in prior literature, this agent-based model is applicable to pricing and investment analyses on large complex networks. The subsequent economic analysis focuses on the source, evolution, measurement, and impact of product differentiation with heterogeneous users on a mixed ownership network (with tolled and untolled roads). Two types of product differentiation in the presence of toll roads, path differentiation and space differentiation, are defined and measured for a base case and several variants with different types of price and capacity competition and with various degrees of user heterogeneity. The findings favor a fixed-rate road pricing policy compared to complete pricing freedom on toll roads. It is also shown that the relationship between net social benefit and user heterogeneity is not monotonic on a complex network with toll roads.Network dynamics, road pricing, autonomous links, privatization, price competition, product differentiation, agent-based transportation model

Agent-based Congestion Pricing and Transport Routing with Heterogeneous Values of Travel Time Savings

An existing agent-based simulation framework and congestion pricing methodology is extended towards a consistent consideration of non-linear, user- and trip-specific values of travel time savings (VTTS). The heterogeneous VTTS are inherent to the model and result from each agent's individual time pressure. An innovative approach is presented which accounts for the non-linear, user- and trip-specific VTTS (i) when converting external delays into congestion tolls and (ii) when generating new transport routes. The innovative pricing and routing methodology is applied to a real-world case study of the Greater Berlin area, Germany. The proposed methodology performs better than assuming a constant value of travel time savings or randomizing the routing relevant costs. The improved consistency of setting congestion toll levels, identifying transport routes and evaluating travel plans is found to result in a higher system welfare

Bicycle traffic and its interaction with motorized traffic in an agent-based transport simulation framework

Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is furthermore enhanced to account for traffic events that emerge in the simulation (e.g. passings by cars), which have an additional impact on cyclists’ decisions. Inspired by an evolutionary computing perspective, a randomizing router was implemented to enable cyclists to find realistic routes. It is discussed in detail why this approach is both feasible in practical terms and also conceptually consistent with MATSim’s co-evolutionary simulation approach. It is shown that meaningful simulation results are obtained for an illustrative scenario, which indicates that the developed methods will make real-world scenarios more realistic in terms of the representation of bicycle traffic. Based on the exclusive reliance on open data, the approach is spatially transferable

Is marginal emission cost pricing enough to comply with the EU CO2 reduction targets?

From transport economic literature it is known that pricing (environmental) externalities can improve the efficiency of a transport system. However, in real-world politics, policy setting often follows so-called `backcasting' approaches where predefined goals are set, and policy measures are implemented to reach those goals. This study presents, for a specific case study, an parametric approach to identify the gap between toll levels derived from environmental damage cost internalization and toll levels from the goal to reduce global greenhouse gas emissions in the transport sector until 2020 by 20% (avoidance cost approach). For this purpose, the damage costs internalization is applied to a real-world scenario of Munich metropolitan area. The results indicate that the desired reduction in CO2 emissions is not reached. This parametric internalization approach with damage cost estimates from the literature yields toll levels that are by a factor of 5 too low in order to reach the predefined goal. When aiming at overall emission cost reductions by 20%, the damage cost estimates are even by a factor of 10 too low. Furthermore, it is shown that the major contribution to the overall emission reduction stems from behavioral changes of (reverse) commuters rather than from urban travelers; under some circumstances, the latter even increase their CO2 emission levels. Finally, the study indicates that there might be conicting trends for different types of pollutants, i.e. pricing emissions does not necessarily result in a reduction of all pollutant types

An Agent-based Route Choice Model

Travel demand emerges from individual decisions. These decisions, depending on individual objectives, preferences, experiences and spatial knowledge about travel, are both heterogeneous and evolutionary. Research emerging from fields such as road pricing and ATIS requires travel demand models that are able to consider travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g. willingness to switch routes with potential savings) in a differentiated market (by tolls and the level of service). Traditional trip-based models have difficulty in dealing with the aforementioned heterogeneity and issues such as equity. Moreover, the role of spatial information, which has significant influence on decision-making and travel behavior, has not been fully addressed in existing models. To bridge the gap, this paper proposes to explicitly model the formation and spread- ing of spatial knowledge among travelers. An Agent-based Route Choice (ARC) model was developed to track choices of each decision-maker on a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied on both SiouxFalls network and Chicago sketch network. Comparison between ARC and existing models (UE and SUE) on both networks shows ARC is valid and computationally tractable. To be brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of travel demand under an integrated framework.Agent-based model, route choice, traffic assignment, travel demand modeling