The line planning routing game

Schiewe, Alexander; Schiewe, Philine; Schmidt, Marie

doi:10.1016/j.ejor.2018.10.023

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…This approach overcomes the problem that passengers are assigned to sub-optimal routes by the model and prevents capacity conflicts during operation. Schiewe et al (2019) propose a game-theoretical approach where passengers are individual players choosing their routes with the highest travel quality.…”

Section: Related Literaturementioning

confidence: 99%

Modeling and Solving Line Planning with Integrated Mode Choice

Hartleb

Schmidt²,

Huisman

et al. 2021

SSRN Journal

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We present a mixed-integer linear program (MILP) for line planning with integrated mode and route choice. In contrast to existing approaches, the mode and route decisions are modeled according to the passengers' preferences while commercial solvers can be applied to solve the corresponding MILP.The model aims at finding line plans that maximize the profit for the public transport operator while estimating the corresponding passenger demand with choice models. Both components of profit, revenue and cost, are influenced by the line plan. Hence, the resulting line plans are not only profitable for operators but also attractive to passengers. By suitable preprocessing of the passengers' utilities, we are able to apply any choice model for mode choices using linear constraints. We provide and test means to improve the computational performance. In experiments on the Intercity network of the Randstad, a metropolitan area in the Netherlands, we show the benefits of our model compared to a standard line planning model with fixed passenger demand. Furthermore, we demonstrate with the help of our model the possibilities and limitations for operators when reacting to changes in demand in an optimal way.The results suggest that operators should regularly update their line plan in response to changes in travel demand and estimate the passenger demand during optimization.

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Section: Related Literaturementioning

confidence: 99%

Modeling and Solving Line Planning with Integrated Mode Choice

Hartleb

Schmidt²,

Huisman

et al. 2021

SSRN Journal

Self Cite

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“…An efficient solution approach is provided by [BGP07], still, the integrated problem can only be solved for small to medium sized instances. In a capacitated setting it is especially difficult to guarantee that all passengers can travel on shortest paths, see [GS17] for integer programming formulations and [SSS19] for game theoretic approaches. The integration of timetabling with passenger routing came up later, but has been researched intensively since this time, see [SS10, SG13, Sch14, SS15, RSAMB17, BHK17, SS20, PSH21].…”

Section: Literature Review: Planning In Public Transportationmentioning

confidence: 99%

Integrated Optimization of Sequential Processes: General Analysis and Application to Public Transport

Schiewe¹,

Schöbel²

2021

Preprint

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Planning in public transportation is traditionally done in a sequential process: After the network design process, the lines and their frequencies are planned. When these are fixed, a timetable is determined and based on the timetable, the vehicle and crew schedules are optimized. After each step, passenger routes are adapted to model the behavior of the passengers as realistically as possible. It has been mentioned in many publications that such a sequential process is sub-optimal, and integrated approaches, mainly heuristics, are under consideration. Sequential planning is not only common in public transportation planning but also in many other applied problems, among others in supply chain management, or in organizing hospitals efficiently.

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“…The main challenge in a transportation network context is defining a many-to-one model structure where sellers are defined as routes with line capacities and multiple travelers are matched to segments of each route. We use the term "line" in reference to service lines (see Schöbel, 2012;Schiewe et al, 2019) on a route. For example, a route may serve 100 passengers in total but have a line capacity of 10 so that the number never exceeds that amount at any segment and direction, and three lines with the same line capacity may share an infrastructure link such that it cannot exceed 30 passengers on that link.…”

Section: Review Of Assignment Gamesmentioning

confidence: 99%

Route-cost-assignment with joint user and operator behavior as a many-to-one stable matching assignment game

Rasulkhani

Chow

2019

Transportation Research Part B: Methodological

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We propose a generalized market equilibrium model using assignment game criteria for evaluating transportation systems that consist of both operators' and users' decisions. The model finds stable pricing, in terms of generalized costs, and matches between user populations in a network to set of routes with line capacities. The proposed model gives a set of stable outcomes instead of single point pricing that allows operators to design ticket pricing, routes/schedules that impact access/egress, shared policies that impact wait/transfer costs, etc., based on a desired mechanism or policy. The set of stable outcomes is proven to be convex from which assignment-dependent unique user-optimal and operator-optimal outcomes can be obtained. Different user groups can benefit from using this model in a prescriptive manner or within a sequential design process. We look at several different examples to test our model: small examples of fixed transit routes and a case study using a small subset of taxi data in NYC. The case study illustrates how one can use the model to evaluate a policy that can require passengers to walk up to 1 block away to meet with a shared taxi without turning away passengers.Planning for mobility in a smart cities era requires an understanding beyond the route choices of travelers. With the increasing ubiquity of multiple forms of "Mobility as a Service" (MaaS) options to travelers (e.g. conventional fixed route transit, flexible transit, rideshare, carshare, microtransit, ridesourcing) provided by both public agencies and private operators known as "transportation network companies" (TNCs), travel forecast models need to focus on both the decisions of travelers and operators (Djavadian and Chow, 2017a). For example, a person's decision to take one mobility service over another may depend on the travel performance of that service, but the performance in turn depends on the operator's cost allocation decisions to best serve their users. Table 1 illustrates the broad range of cost allocation decisions exemplified by different types of mobility systems and how those decisions impacts costs for users and operators.

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The line planning routing game

Cited by 13 publications

References 38 publications

Modeling and Solving Line Planning with Integrated Mode Choice

Modeling and Solving Line Planning with Integrated Mode Choice

Integrated Optimization of Sequential Processes: General Analysis and Application to Public Transport

Route-cost-assignment with joint user and operator behavior as a many-to-one stable matching assignment game

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