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Public Policy, Innovation and Strategy
John D. Graham
John D. Graham
Edited by Graham Currie
This Handbook of Public Transport Research aims to provide a comprehensive overview of the latest research in a growing field: the field of research on urban public transport. The quantity of public transport related research papers has doubled in the last nine years. Why? For two reasons. First, researchers have been increasingly inspired by the topic. It is an applied and practical topic affecting the quality of life of billions of people. It is also a field with significant challenges, seeking new and original solutions. These challenges range from the difficult interface of engineering, operations and human perceptions in user satisfaction and performance management, to the tricky balance between prudent financial management, operations planning and the social access goals making subsidies essential. These challenges require a multi-disciplinary perspective to wicked problems in Engineering, Planning, Psychology and Design, which is why the field is intellectually as well as tactically challenging. The foundation of many of these challenges is the conflicting congestion and environmental relief, and the social equity objectives that justify public transport in cities.
Tao Liu and Avishai (Avi) Ceder
In this chapter we refer to the public transport (PT) operations planning process of a fixed-route system such as bus, rail and passenger ferries. This process commonly includes four basic components, divided into three different levels and usually performed in sequence: (1) network design; (2) timetable development; (3) vehicle scheduling; and (4) crew scheduling and rostering. The framework of this process is shown in Figure 18.1. It is preferable that all four activities be planned simultaneously in order to exploit system capability to the greatest extent and maximize system productivity and efficiency (Ceder 2016). However, since this integrated planning process is extremely cumbersome and complex, especially for medium and large-scale PT agencies, separated treatment is required for each component, with the outcome of one fed as an input into the next component. From the perspective of PT agencies, the highest cost items in the budget are vehicle capital and operating costs, driver wages and fringe benefits. Therefore, it is not surprising to learn that most of the commercially available PT scheduling software packages concentrate primarily on vehicle and crew scheduling activities. In the last fifty years, a considerable amount of effort has been invested in the computerization of the above four components in order to provide more efficient, controllable and responsive PT services. This chapter focuses on the third PT operations-planning component: vehicle scheduling, which is one of the problems at the operational-planning level. The PT vehicle scheduling problem (VSP) refers to the problem of determining the optimal allocation of vehicles to carry out all the trips of a given timetable. A chain of trips is assigned to each vehicle, although some of them may be deadheading (DH) or empty trips in order to attain optimality. The assignment of vehicle chains to garages should be determined in an efficient manner. The major objective of the PT VSP is to minimize fleet size or, correspondingly, to minimize the total cost comprised of fixed costs (acquisition, salaries, administration, etc.) and variable costs (maintenance, fuels, supplies, etc.). The number of feasible solutions to this problem is extremely high, especially in the case of multiple depots.
Commodities and People, Capital, Information and Technology
Edited by Robin Hickman, Beatriz Mella Lira, Moshe Givoni and Karst Geurs
Marcela A. Munizaga
After many years of data scarcity in transportation-related sciences, we have now entered the era of big data. Large amounts of data are available from GPS devices, mobile phone traces, payment transactions, social media, and other sources. The opportunities that this new availability presents are enormous. High-quality data is available at very low or negligible cost. These data can be used to develop new tools, to explore and understand travel behavior and to formulate new policies. However, the challenges are also big: the access to the data is not guaranteed, confidentiality has to be considered, the capacity of processing and enriching these databases has to be developed, and only then will they become really useful for decision-making and for the definition of public policies. This chapter presents an overview of the current state of play, and discusses the future perspectives, focusing on the challenges of building new predictive models.