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Short-Term Prediction of Ridership on Public Transport with Smart Card Data

Public transport operators are collecting massive amounts of data from smart card systems. In the Netherlands, every passenger checks in and checks out; this system creates detailed records of demand patterns. In buses and trams, users check in and check out in the vehicle; this factor provides good information on route choice. Options for analyzing smart card data and performing what-if analyses with transport planning software were explored. On the basis of big data, this new generation of transport demand models added to the existing range of transport demand models and approaches. The goal was to provide public transport operators with a simple (easy-to-build) model to perform what-if analyses. The data were converted to passengers per line and an origin–destination matrix between stops. This matrix was assigned to the network to repro-duce the measured passenger flows, and then what-if analysis became possible. With fixed demand, line changes could be investigated. With the introduction of an elastic demand model, changes in the level of service realistically affected passenger numbers. This method was applied to a case study in The Hague, Netherlands. Smart card data were imported into a transport model and connected with the network. The tool proved to be valuable to operators, who gained insights into the effects of small changes.

Model complexities and requirements for multimodal transport network design: Assessment of classical, state-of-the-practice, and state-of-the-research models

In the aim for a more sustainable transport system, governments try to stimulate multimodal trip making by facilitating smooth transfers between modes. The assessment of related multimodal policy measures requires transport models that are capable of handling the complex nature of multimodality. This complexity sets requirements for adequate modeling of multimodal travel behavior and can be categorized into three classes that are related to the range and combinatorial complexity of the available alternatives, the mathematical complexity of modeling the choice between them, and the complex effect of demand–supply interactions. Classical modeling approaches typically fail to meet these requirements and state-of-the-practice approaches only partly fulfill them. Therefore, the underlying hypothesis of this study was that the application of such models in network design implied an ill-advised decision-making process. Thus, these modeling approaches, as well as the promising state-of-the-research supernetwork approach, were conceptually compared with each other. Requirements for multimodality were constructed, and all three models were tested on the way in which these requirements can be met. The findings of this conceptual comparison were supported by realistic examples in the real-world transport network of the Amsterdam Metropolitan Area in the Netherlands. The theoretical shortcomings of the classical and state-of-the-practice approach were shown to indeed result in implausible predictions of multimodal travel behavior. The flexibility of the supernetwork approach, however, was very capable of describing the expected effect of supply changes on travel behavior in most situations. This study illustrates the urgency for applying sound multimodal modeling approaches in network design studies.

Multi-objective optimization of traffic externalities using tolls

Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally distribute traffic in a network with aim of combating some traffic externalities such as congestion, emission, noise, safety issues. Formulating the multi-objective toll problem as a one point solution problem fails to give the general overview of the objective space of the MOP. Therefore, in this paper we develop a game theoretic approach that gives the general overview of the objective space of the multiobjective problem and compare the results with those of the wellknown genetic algorithm non-dominated sorting genetic algorithm II (NSGA-II). Results show that the game theoretic approach presents a promising tool for solving multi-objective problems, since it produces similar non-dominated solutions as NSGA-II, indicating that competing objectives (or stakeholders in the game setting) can still produce Pareto optimal solutions. Most fascinating is that a range of non-dominated solutions is generated during the game, and almost all generated solutions are in the neighborhood of the Pareto set. This indicates that good solutions are generated very fast during the game.

Multi-objective transportation network design: Accelerating search by applying ε-NSGAII

The optimization of infrastructure planning in a multimodal passenger transportation network is formulated as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train stations and the frequency of public transport lines. For a real life case study the Pareto set is estimated by the Epsilon Non-dominated Sorting Genetic Algorithm (ε-NSGAII), since due to high computation time a high performance within a limited number of evaluated solutions is desired. As a benchmark, the NSGAII is used. In this paper Pareto sets from runs of both algorithms are analyzed and compared. The results show that after a reasonable computation time, ε-NSGAII outperforms NSGAII for the most important indicators, especially in the early stages of algorithm executions.