Passengers, Crowding and Complexity : Models for passenger oriented public transport

Passengers, Crowding and Complexity was written as part of the Complexity in Public Transport (ComPuTr) project funded by the Netherlands Organisation for Scientific Research (NWO). This thesis studies in three parts how microscopic data can be used in models that have the potential to improve utilization, while preventing excess crowding. _In the first part_, the emergence of crowding caused by interactions between the behavior of passengers and the public transport operators who plan the vehicle capacities is modeled. Using simulations the impact of the information disclosed to the passengers by public transport operators on the utilization and passenger satisfaction is analyzed. A quasi-experiment with a large group of students in a similar setting finds that four types of behavior can be observed. _In the second part_, algorithms that can extract temporal and spatial patterns from smart card data are developed and a first step to use such patterns in an agent based simulation is made. Furthermore, a way to generate synthetic smart card data is proposed. This is useful for the empirical validation of algorithms that analyze such data. _In the third and final part_ it is considered how individual decision strategies can be developed in situations where there exists uncertainty ab

Optimization Approaches for the Traveling Salesman Problem with Drone

The fast and cost-efficient home delivery of goods ordered online is logistically challenging. Many companies are looking for new ways to cross the last-mile to their customers. One technology-enabled opportunity that recently has rec

Vehicle scheduling based on a line plan

We consider the following problem: given a set of lines in a public transportation network with their round trip times and frequencies, a maximum number of vehicles and a maximum number of lines that can be combined into a vehicle circulation, does there exist a set of vehicle circulations that covers all lines given the constraints. Solving this problem provides an estimate of the costs of operating a certain line plan, without having to compute a timetable first. We show that this problem is NP-hard for any restriction on the number of lines that can be combined into a circulation which is equal to or greater than three. We pay special attention to the case where at most two lines can be combined into a circulation, which is NP-hard if a single line can be covered by multiple circulations. If this is not allowed, a matching algorithm can be used to find the optimal solutions, which we show to be a 16/15-approximation for the case where it is allowed. We also provide an exact algorithm that is able to exploit low tree-width of the so-called circulation graph and small numbers of vehicles required to cover single circulations

Dynamic programming approaches for the traveling salesman problem with drone

A promising new delivery model involves the use of a delivery truck that collaborates with a drone to make deliveries. Effectively combin

Determining and Evaluating Alternative Line Plans in (Near) Out-of-Control Situations

From time to time, large disruptions cause heavily utilized railway networks to get in a state of (near) out-of-control, in which hardly any trains are able to run as the result of a lack of accurate and up-to-date information available to dispatchers. In this paper, we develop and test disruption management strategies for dealing with these situations. First, we propose an algorithm that finds an alternative line plan that can be operated in the affected part of the railway network. As the line plan should be feasible with respect to infrastructural and resource restrictions, we integrate these aspects in the algorithm in a Benders'-like fashion. Second, to operate the railway system within the disrupted region, we propose several local train dispatching strategies requiring varying degrees of exibility and coordination. Computational experiments based on disruptions in the Dutch railway network indicate that the algorithm performs well, finding workable and passenger oriented line plans within a couple of minutes. Moreover, we also demonstrate in a simulation study that the produced line plans can be operated smoothly without depending on central coordination

A Next Step in Disruption Management: Combining Operations Research and Complexity Science

Railway systems occasionally get into a state of out-of-control, meaning that there is barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. These situations can either be caused by large disruptions or unexpected propagation and accumulation of delays. Because of the large number of aected resources and the absence of detailed, timely and accurate information, currently existing methods cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework aiming at reducing the impact of these situations and - if possible - avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations using tools from complexity science and (ii) a set of rescheduling measures robust against the features of out-of-control situations, using tools from operations research

A next step in disruption management: combining operations research and complexity science

Railway systems occasionally get into a state of being out-of-control, meaning that barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing disruption management techniques cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework combining techniques from complexity science and operations research, aiming at reducing the impact of these situations and—if possible—avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations, (ii) isolating a specific region such that delay stops propagating, and (iii) the app

A Self-Organizing Policy for Vehicle Dispatching in Public Transit Systems with Multiple Lines

In this paper, we propose and analyze an online, decentralized policy for dispatching vehicles in a multiline public transit system. In the policy, vehicles arriving at a terminal station are assigned to the lines starting at the station in a round-robin fashion. Departure times are selected to minimize deviations from a certain target headway. We prove that this policy is self-organizing: given that there is a sufficient number of available vehicles, a timetable spontaneously emerges that meets the target headway of every line. Moreover, in case one of the vehicles breaks down, the remaining vehicles automatically redistribute over the network to re-establish such a timetable. We present both theoretical and numerical results on the time until a stable state is reached and on how quickly the system recovers after the breakdown of a vehicle. These promising results suggest that our self-organizing policy could be useful in situations where centralized dispatching is impractical or simply impossible due to an abundance of disruptions or the absence of information systems