Bilevel Optimization for On-Demand Multimodal Transit Systems

This study explores the design of an On-Demand Multimodal Transit System (ODMTS) that includes segmented mode switching models that decide whether potential riders adopt the new ODMTS or stay with their personal vehicles. It is motivated by the desire of transit agencies to design their network by taking into account both existing and latent demand, as quality of service improves. The paper presents a bilevel optimization where the leader problem designs the network and each rider has a follower problem to decide her best route through the ODMTS. The bilevel model is solved by a decomposition algorithm that combines traditional Benders cuts with combinatorial cuts to ensure the consistency of mode choices by the leader and follower problems. The approach is evaluated on a case study using historical data from Ann Arbor, Michigan, and a user choice model based on the income levels of the potential transit riders

Integrating Academic and Everyday Learning Through Technology: Issues and Challenges for Researchers, Policy Makers and Practitioners

This paper builds on work undertaken over a number of years by a group of international researchers with an interest in the potential of connecting academic and everyday practices and knowledge. Drawing extensively on literature and our own work, we first discuss the challenges around defining informal learning, concluding that learning is multidimensional and has varying combinations of formal and informal attributes. We then highlight the potential of technology for integrating formal and informal learning attributes and briefly provide some exemplars of good practice. We then discuss in depth the challenges and issues of this approach to supporting learning from the perspective of pedagogy, research, policy and technology. We also provide some recommendations of how these issues may be addressed. We argue that for the learner, integration of formal and informal learning attributes should be an empowering process, enabling the learner to be self-directed, creative and innovative, taking learning to a deeper level. Given the complexity of the learning ecosystem, this demands support from the teacher but also awareness and understanding from others such as parents, family, friends and community members. We present a conceptual model of such an ecosystem to help develop further discussions within and between communities of researchers, policy makers and practitioners

Integer Programming Formulation of the Bilevel Knapsack Problem

The Bilevel Knapsack Problem (BKP) is a hierarchical optimization problem in which the feasible set is determined by the set of optimal solutions of parametric Knapsack Problem. In this paper, we propose two stages exact method for solving the BKP. In the first stage, a dynamic programming algorithm is used to compute the set of reactions of the follower. The second stage consists in solving an integer program reformulation of BKP. We show that the integer program reformulation is equivalent to the BKP. Numerical results show the efficiency of our method compared with those obtained by the algorithm of Moore and Bar

Fare inspection patrols scheduling in transit systems using a Stackelberg game approach

This study analyzes the scheduling of unpredictable fare inspections in proof-of-payment transit systems, where the transit operator chooses a collection of patrol paths (one for each patrol) every day with some probability in order to avoid any regularity that could be exploited by opportunistic passengers. We use a Stackelberg game approach to represent the hierarchical decision-making process between the transit operator and opportunistic passengers, whose decision on whether to evade the fare depends on the inspection probabilities set by the transit operator. Unlike previous work, we use an exact formulation of the inspection probabilities that allows us to develop new heuristics for the fare inspection scheduling problem, and to assess their solution quality in terms of their optimality gap