Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm

Arbex, Renato Oliveira; Cunha, Cláudio Barbieri da

doi:10.1016/j.trb.2015.06.014

Cited by 152 publications

(74 citation statements)

References 43 publications

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“…For example, Mauttone and Urquhart () solved a transit route and frequencies design problem to minimize passengers’ cost and operators’ cost. Arbex and da Cunha () proposed an efficient heuristic method for the multi‐objective transit route and frequencies design problems. However, finding the nondominated solutions that represent the trade‐off between users and operators can be challenging and time consuming, so we only consider a single‐objective model in the rolling horizon framework.…”

Section: Model Formulationmentioning

confidence: 99%

Solving the first‐mile ridesharing problem using autonomous vehicles

Chen

Wang

Meng

2019

Computer aided Civil Eng

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Autonomous vehicles (AVs) are paving a way to reshape the operation and management of urban transportation systems by improving travel mobility. In this study, we propose to use AVs for solving the first‐mile (FM) problem that aims to transport passengers from their homes to metro stations. Passengers submit travel requests in advance and a fleet operator dispatches AVs and arranges ridesharing in a rolling horizon framework. The ridesharing is implemented by assigning one AV to serve several requests subject to the vehicle capacity, the maximum travel time, and the accessibility constraints. A mixed integer linear programming (MILP) model is formulated to determine the AV dispatch and ridesharing schemes for the minimum operational costs. Then, another MILP model with the objective of the maximum user satisfaction is formulated for the purpose of comparison. A cluster‐based solution method is designed to deal with the large‐scale FM problem. Extensive numerical experiments are conducted to demonstrate the application of the proposed method. Results show that ridesharing can reduce both required AV fleet size and vehicle traveled miles. The proposed solution method is able to solve the problem efficiently and fulfil the requirement of online computation.

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Section: Model Formulationmentioning

confidence: 99%

Solving the first‐mile ridesharing problem using autonomous vehicles

Chen

Wang

Meng

2019

Computer aided Civil Eng

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“…For other kinds of constraints that cannot be converted into the three types examined here, the dynamic generation properties of solving the GA-based network path may be helpful in solving such problems according to the generation-refinement paradigm (e.g., multiconstraint transportation network design [42,43] and transit network analysis [12,44]). With the network represented in the GA framework, the route in the network can be directly generated and refined according to the properties of the algebra system.…”

Section: Potential Application Of This Template-based Approachmentioning

confidence: 99%

Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

Luo

et al. 2018

IJGI

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The process of searching for a dynamic constrained optimal path has received increasing attention in traffic planning, evacuation, and personalized or collaborative traffic service. As most existing multiple constrained optimal path (MCOP) methods cannot search for a path given various types of constraints that dynamically change during the search, few approaches for dynamic multiple constrained optimal path (DMCOP) with type II dynamics are available for practical use. In this study, we develop a method to solve the DMCOP problem with type II dynamics based on the unification of various types of constraints under a geometric algebra (GA) framework. In our method, the network topology and three different types of constraints are represented by using algebraic base coding. With a parameterized optimization of the MCOP algorithm based on a greedy search strategy under the generation-refinement paradigm, this algorithm is found to accurately support the discovery of optimal paths as the constraints of numerical values, nodes, and route structure types are dynamically added to the network. The algorithm was tested with simulated cases of optimal tourism route searches in China's road networks with various combinations of constraints. The case study indicates that our algorithm can not only solve the DMCOP with different types of constraints but also use constraints to speed up the route filtering.Two different types of dynamics can increase the complexity of the DMCOP problem. With type I dynamics, the network condition, such as the weight or topology, changes during optimal path searching; the constraints are not altered. For example, in adaptive dynamic traffic navigation, the optimal path should be dynamically generated with the traffic conditions, where the weights are dynamically changed according to the current traffic status. With type II dynamics, the network conditions are not changed; however, the constraints used to restrict the searching of the optimal path change with time. One example is event-forced constraints, which are not determined unless a certain kind of event (e.g., emergency or user interactions) occurs. In this way, the constraints should be changed according to the specific schedule of the event dynamically occurring. Since these different constraints cannot be known in advance, these constraints should be added dynamically during route planning [15]. For instance, in a typical scenario of a long-distance self-driving tour, the navigation may first start from route planning with a start point and an end point. During the travel, users may add location constraints [16], reliability constraints [17], turn constraints [18] for intercity traffic and cycle routes [19], or satisfaction constraints [20]. Certain emergency cases may also exist that require the planned route to be dynamically adjusted in a short amount of time according to a series of constraints.Previous initial studies have addressed the solution of DMCOP with type I dynamics (i.e., network status changes) [21]. Schott and Staples ...

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“…The upper-level problem aims to minimize the total number of passenger transfers, and strict capacity constraints are added to the lower-level problem. Arbex and da Cunha [21] proposed an alternating objective genetic algorithm to efficiently solve the multi-objective transit network design problem, aiming to minimize both passengers' and operators' costs, and the results indicated that the algorithm led to improved solutions. Zhang et al [22] presented an improved matrix multiplication method to solve the all-pairs-shortest-path problem in the pulse coupled neural network.…”

Section: Problem Descriptionmentioning

confidence: 99%

Optimization of Trip-end Networks and Ride Price for Express Coach Systems in the High-speed Rail Era

Yang

Jiannan

2017

PROMET

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Express coach (EC) lost a considerable share of passengers after high-speed rail (HSR) was implemented. This paper proposes a door-to-door operation mode for the EC system and builds a model to design an EC trip-end network in the origin city with the aim of maximizing the EC's daily operating profit. A case study is undertaken, and the results show that the operating profit of the EC system first increases and then decreases with the growth of the trip-end routes.In the HSR era, door-to-door operation can effectively guarantee the market share and operating profits of the EC.

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Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm

Cited by 152 publications

References 43 publications

Solving the first‐mile ridesharing problem using autonomous vehicles

Solving the first‐mile ridesharing problem using autonomous vehicles

Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach

Optimization of Trip-end Networks and Ride Price for Express Coach Systems in the High-speed Rail Era

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