Integrated risk management of safety and development on transportation corridors

Thekdi, Shital; Lambert, James H.

doi:10.1016/j.ress.2014.11.015

Cited by 32 publications

(8 citation statements)

References 18 publications

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“…In this paper, the freight communication capacity is expressed by the freight turnover between the nodes in the corridor, which reflects the economic flow relationship between the cities. The freight communication capacity is calculated as follows: (13) where E ij is the capacity of freight transportation between node city i and j, Q ij is the freight total volume between node city i and j through various modes of transportation, Q ij is the average value of the exchange volume between node city i and j, l ij is the shortest path length between node city i and j depending on the city coordinates and adjacency matrix, and l ij is the shortest average value. The corridor line variable y ij is introduced, and its calculation formula is formula (11).…”

Section: D: Line Importance Modelmentioning

confidence: 99%

Identification and Optimization Models for a Freight-Integrated Transportation Corridor With Line Importance and Freight Communication Capability

Wang

Chu

et al. 2019

IEEE Access

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Identifying and optimizing a freight-integrated transportation corridor is a complicated problem. Currently, corridor structure optimization approaches, such as the mode split rate of freight transportation in the corridor, are topics of discussion. However, identifying the corridor lines and clustering the lines into the same corridor are the keys to reasonably optimizing the corridor. To this end, this paper establishes a corridor selection model from the importance of routes and freight communication capacity point of view and designs a corridor-clustering algorithm based on the DBSCAN clustering algorithm to optimize the transportation corridor. This method is applied to the example of freight transportation in Jilin Province, China, to demonstrate the performance of this model and algorithm.

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Section: D: Line Importance Modelmentioning

confidence: 99%

Identification and Optimization Models for a Freight-Integrated Transportation Corridor With Line Importance and Freight Communication Capability

Wang

Chu

et al. 2019

IEEE Access

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“…Regarding the assessment of logistics and transportation infrastructure, we notice that much recent work uses newly collected data sets that were not available until recently, thanks to recent advances in on-site sensing and data collection. Thekdi and Lambert (2015) introduced the Corridor Trace Analysis tool for assessing the impact of developments on adjacent land on road transport networks. The tool prioritised corridor segments that are vulnerable to adjacent land development.…”

Section: Simulationmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

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When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operatewithin the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable.On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tacticaland operational planning.In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions.More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure.Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand thata facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge forinvestors and operators of rail inter-modal terminals.We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.

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“…In the same way, the order of importance of the lower-level indicators of the two levels of economic sustainability and urban coordination sustainability can be obtained [43]. According to the principle of establishing the index system, the following complete indicator models are obtained [44], as shown in Table 4.…”

Section: Use Ahp To Determine Index Level Indicatorsmentioning

confidence: 99%

Establishment and Application of an Evaluation System for the Sustainable Development of Subway Traffic

Geng¹,

Vojtasík²

2018

Preprint

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According to the characteristics of the sustainable development of subway traffic, the establishment process of an evaluation index system is determined, and the evaluation method and basis are defined. The evaluation index system is established from the aspects of subway traffic sustainability, economic sustainability and urban coordination sustainability. The comprehensive evaluation method of the analytic hierarchy process (AHP) model is used to calculate the weights of comprehensive evaluation indexes at each level. Finally, the sustainable development model of Shijiazhuang subway traffic is evaluated. The results show that the proposed evaluation system and model reflect the degree of sustainable development of subway traffic and can be used for reference in the evaluation of regional subway traffic sustainable development.

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Integrated risk management of safety and development on transportation corridors

Cited by 32 publications

References 18 publications

Identification and Optimization Models for a Freight-Integrated Transportation Corridor With Line Importance and Freight Communication Capability

Identification and Optimization Models for a Freight-Integrated Transportation Corridor With Line Importance and Freight Communication Capability

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Establishment and Application of an Evaluation System for the Sustainable Development of Subway Traffic

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