Review of Urban Transportation Network Design Problems Based on CiteSpace

Jia, Guo-Ling; Rong-guo, MA; Hu, Zhi‐Hua

doi:10.1155/2019/5735702

Cited by 44 publications

(29 citation statements)

References 80 publications

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“…In the literature, studies concerning TNDP addresses strategic issues (route generation, network design, and frequency setting) and tactical issues (timetabling of transit lines and vehicle and crew scheduling); seminal works in this field are [3][4][5][6][7][8][9][10], and a recent review of the mathematical models and solution methods for the TNDP can be found in [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem

et al. 2020

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The research presented in this paper proposes a Particle Swarm Optimization (PSO) approach for solving the transit network design problem in large urban areas. The solving procedure is divided in two main phases: in the first step, a heuristic route generation algorithm provides a preliminary set of feasible and comparable routes, according to three different design criteria; in the second step, the optimal network configuration is found by applying a PSO-based procedure. This study presents a comparison between the results of the PSO approach and the results of a procedure based on Genetic Algorithms (GAs). Both methods were tested on a real-size network in Rome, in order to compare their efficiency and effectiveness in optimal transit network calculation. The results show that the PSO approach promises more efficiency and effectiveness than GAs in producing optimal solutions.

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Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem

et al. 2020

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“…Among available strategies, providing dedicated lanes to autonomous vehicles using existing infrastructure is a flexible and low-cost one. It is a tactical decision which can benefit the network through existing resources [10]. However, conversion of a general purpose lane to a dedicated lane may lead to public dissatisfaction.…”

Section: Introductionmentioning

confidence: 99%

Optimum Location of Autonomous Vehicle Lanes: A Model Considering Capacity Variation

Movaghar

Mesbah

Habibian

2020

Mathematical Problems in Engineering

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This paper proposes a model to find the optimal location of autonomous vehicle lanes in a transportation network consisting of both Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) while accounting for the roadway capacity variation. The main contribution of the model is considering a generalized definition of capacity as a function of AV proportion on a link and incorporating it into the network design problem. A bilevel optimization model is proposed with total travel time as the objective function to be minimized. At the upper-level problem, the optimal locations of AV lanes are determined, and at the lower level which is a multiclass equilibrium assignment, road users including both AVs and HDVs seek to minimize their individual travel times. It is shown that if capacity variation is ignored, the effect of AV lane deployment can be misleading. Since there will be a long transition period during which both AVs and HDVs will coexist in the network, this model can help the network managers to optimally reallocate the valuable road space and better understand the effects of AV lane deployment at the planning horizon as well as during the transition period. Employing this model as a planning tool presents how the proposed AV lane deployment plan could consider the AV market penetration growth during the transition period. Numerical analysis based on the Sioux Falls network is presented in two cases with and without variable capacity to illustrate the application of this model. At the 60% penetration rate of AVs, the improvement in total travel time was 3.85% with a fix capacity while this improvement was 9.88% with a variable capacity.

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“…Hence, Traffic lights optimization using adaptive and intelligent algorithms in conjunction with smart sensing is a must under these circumstances. This comes under design optimization, where smart algorithms such as genetic and fuzzy logic algorithms are employed to improve an existing transportation network as a function of increasing level of traffic flow levels, resulting in traffic congestion, delays, higher fuel consumption, air pollution and increase probability of traffic incidents [1][2].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Genetic Algorithm Performance for Effective Traffic Lights Control using Balancing Technique (GABT)

Iskandarani¹

2020

IJACSA

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Genetic Algorithm (GA) is implemented and simulation tested for the purpose of adaptable traffic lights management at four roads-intersection. The employed GA uses hybrid Boltzmann Selection (BS) and Roulette Wheel Selection techniques (BS-RWS). Selection Pressure (SP) and Population (Pop) parameters are used to tune and balance the designed GA to obtain optimized and correct control of passing vehicles. A very successful implementation of such parameters resulted in obtaining minimum number of Iterations (IRN) for a wide spectrum of SP and Pop. The algorithm is mathematically modeled and analyzed and a proof is obtained regarding the condition for balanced GA. Such Balanced GA is most useful in traffic management for an optimized Intelligent Transportation Systems, as it requires minimum iterations for convergence with faster dynamic controlling time.

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Review of Urban Transportation Network Design Problems Based on CiteSpace

Cited by 44 publications

References 80 publications

A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem

A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem

Optimum Location of Autonomous Vehicle Lanes: A Model Considering Capacity Variation

Optimizing Genetic Algorithm Performance for Effective Traffic Lights Control using Balancing Technique (GABT)

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