Intersection Modeling for Highway Alignment Optimization

Kim, Eungcheol; Jha, M. K.; Lovell, David J.; Schonfeld, Paul

doi:10.1111/j.1467-8667.2004.00342.x

Cited by 38 publications

(20 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jha [14], Jha and Schonfeld [15] and Jha et al [16] extended the model by integrating a GIS to better accommodate the complex topological and environmental features. Kim [17] and Kim et al [18] developed methods for incorporating the cost of major structures, i.e., bridges and tunnels. Kang et al [19] further improved the GAbased solution algorithm by introducing the Feasible Gates approach.…”

Section: Introductionmentioning

confidence: 99%

Concurrent Optimization of Rail Transit Alignments and Station Locations

Lai¹,

Schonfeld

2016

Urban Rail Transit

Self Cite

View full text Add to dashboard Cite

Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, and various other system characteristics. Most of the previous rail transit route optimization studies have focused on the alignment design between predetermined stations, whereas a practical design process has to account for the complex interactions among railway alignments and station locations. This paper proposes a methodology for concurrently optimizing station locations and the rail transit alignment connecting those stations, by accommodating multiple system objectives, satisfying various design constraints, and integrating the analysis models with a geographical information system database. The methodology incorporates demand and station costs in the evaluation framework and employs a genetic algorithm for optimizing the decision variables for station locations, station types, and track alignments. It is expected that transit planners may greatly benefit from the proposed methodology, with which they can conveniently and efficiently optimize candidate alternatives. The Baltimore Red Line is used as a case study to demonstrate how the model can find very good solutions in regions with complex geography.

show abstract

Section: Introductionmentioning

confidence: 99%

Concurrent Optimization of Rail Transit Alignments and Station Locations

Lai¹,

Schonfeld

2016

Urban Rail Transit

Self Cite

View full text Add to dashboard Cite

show abstract

“…A horizontal highway alignment basically comprises of tangent sections, circular curve sections, and transition curve sections that connect tangent and curve sections [8][9][10]. The orientation of these design sections are controlled by Point of Intersections (PI) of the alignment [8,9].…”

Section: Methodsmentioning

confidence: 99%

Integrating highway alignment design capability to the Interactive Highway Safety Design Model (IHSDM): a two-lane highway case study

Maji¹,

Jha²,

Kühn³

2006

WIT Transactions on the Built Environment, Vol 89

View full text Add to dashboard Cite

Interactive Highway Safety Design Model (IHSDM) is a suite of software analysis and decision-support tools, widely used to check the design consistency in response to required safety and operational performance level along two-lane rural highways. It can only identify the inconsistent sections along a given highway. Therefore, after every check, the inconsistent sections are redesigned and checked again to ensure consistency. This process is manual and time consuming. The objective of this research is to incorporate the highway alignment design capability to the IHSDM. This will reduce the manual repetitive steps involved in the highway design and checking. In this study we have used highway geometric design properties and integrated it with IHSDM such that the inconsistent sections of the highway are replaced with sections having optimal safety and operational performance level. This method, first identifies the inconsistent sections from the given highway alignment using IHSDM and then replaces them with new, most appropriate sections. This process continues till all inconsistent sections are replaced. A real-world problem is solved in this paper using the proposed method.

show abstract

“…Thus, many good possible alternatives may be overlooked. To overcome such limitations, a highway alignment optimization (HAO) model has been extensively developed since 1998 (Jong, 1998; Jong et al, 2000; Jong and Schonfeld, 2003; Jha and Schonfeld, 2004; Kim et al, 2004; Kang et al, 2007; Kang, 2008). It integrates genetic algorithms (GAs) and Geographic Information System (GIS) for alignment optimization.…”

Section: Introductionmentioning

confidence: 99%

Prescreening and Repairing in a Genetic Algorithm for Highway Alignment Optimization

Kang¹,

Schonfeld

Yang

2008

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

The method of handling infeasible solutions in an evolutionary search algorithm [e.g., genetic algorithms (GAs)] is crucial to the effectiveness of the solution search process. This problem arises because solution search steps, techniques, and operators used in GAs (such as reproduction, mutation, and recombination) are normally "blind" to the constraints, and thus GAs can generate solutions that do not satisfy the requirements of the problems. In GA-based highway alignment optimization (HAO), many infeasible solutions, which violate model constraints, are also possibly generated, and evaluation of such solutions is wasteful. This study focuses on ways to avoid wasting computation time on evaluating infeasible solutions generated from the GA-based HAO, and develops a prescreening and repairing (P&R) method for an efficient search of highway alignments.The key idea of the P&R method is to repair (before the very detailed alignment evaluation) any candidate alignments whose violations of design constraints can be fixed with reasonable modifications. However, infeasible alignments whose violations of constraints are too severe to repair are discarded (prescreened) before any detailed evaluation procedure is applied. The proposed P&R method is simple, but significantly improves computation time and solution quality in the GA-based HAO process. Such improvements are demonstrated with a test example for a real road project. Through the example study, it is shown that the model incorporating the P&R method can find a good solution much faster (by approximately 23%) than the model with the conventional . penalty method. In addition, the P&R method allows the model to evaluate about 70% more solutions than that it can evaluate with the penalty method for the same number of generations.

show abstract

Intersection Modeling for Highway Alignment Optimization

Cited by 38 publications

References 14 publications

Concurrent Optimization of Rail Transit Alignments and Station Locations

Concurrent Optimization of Rail Transit Alignments and Station Locations

Integrating highway alignment design capability to the Interactive Highway Safety Design Model (IHSDM): a two-lane highway case study

Prescreening and Repairing in a Genetic Algorithm for Highway Alignment Optimization

Contact Info

Product

Resources

About