The α-reliable path problem in stochastic road networks with link correlations: A moment-matching-based path finding algorithm

Chen, Peng; Tong, Rui; Lu, Guangquan; Wang, Yunpeng

doi:10.1016/j.eswa.2018.05.022

Cited by 46 publications

(17 citation statements)

References 52 publications

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“…The proportion of employed members in household is nearly 90%, which might indicate that the employment rate is high, and the large group of household members are at working age. Travel time is an important attribute that can be used to analyze travel distribution and pathfinding [48,49]. It can be estimated that the average number of trips per person is about three, and the average commute travel time is 22-23 min through the personal travel data.…”

Section: Descriptive Analysis Of Sample Datamentioning

confidence: 99%

Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China

Shi

2018

Sustainability

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With the rapid increase of motorization in China, transitions have taken place in regards to traditional private transportation modes. This paper aims to understand four types of vehicle ownership within a household, including automobile, motorcycle, electric bicycle and human-powered bicycle. This study presents a cross-sectional multivariate ordered probit model, with a composite marginal likelihood estimation approach that accommodates the effects of explanatory variables, and capturing the dependence among the propensity to household vehicle ownership. The sample data are obtained from the residents’ household travel survey of Xiaoshan District, Hangzhou, in 2015, which can analyze the significant effects of sociodemographic attributes and built environment attributes. Interestingly, the major findings suggest that: (1) The households with higher income tend to own more automobiles, yet the effect is not obvious with a small value of elasticity, which is similar to developed countries. (2) The household education level, which takes a positive effect on automobile ownership, is a more elastic factor than income. (3) The higher population density contributes to less ownership of automobiles and motorcycles, due to traffic congestions and parking challenges. (4) There is a large substitutive relation between automobile and electric bicycle/motorcycle, and the vehicle ownership of electric bicycle/motorcycle and bicycle are mutually promoted, while motorcycle and electric-bicycle are mutually substituted.

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Section: Descriptive Analysis Of Sample Datamentioning

confidence: 99%

Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China

Shi

2018

Sustainability

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“…At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them produce the children for the next generation (Peng et al, 2018). Over successive generations, the population "evolves" toward an optimal solution (Chen, Tong, Lu, & Wang, 2018;Flick, 2018;Mousavi & Behzadi, 2019;Nasr, El-Bahnasawy, Attiya, & El-Sayed, 2018).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Introducing a Novel Method to Solve Shortest Path Problems Based on Structure of Network Using Genetic Algorithm

Behzadi

Kolbadinejad²

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The shortest path problem is widely applied in transportation, communication and computer networks. It addresses the challenges of determining a path with minimum distance, time or cost from a source to the destination. Network analysis provides strong decision support for users in searching shortest path. A lot of algorithms are designed for solving shortest path but most of them did not considered the conditions of the networks. Genetic Algorithm is a kind of Algorithm that has a lot of efficiency and it can be used for solving many kinds of problems. It also can be used based on the condition of the problems.In this paper, Genetic Algorithm is used for solving shortest path in multistage process planning (MPP) problem. New mutations as well as crossover parameters are defined for each network based on the conditions of them. The results of our experimental demonstrate the effectiveness of the models.

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“…Chen et al in [9] proposed the moment-matching-based hybrid genetic algorithm (MHGA) to search for the reliable shortest path (RSP) in stochastic road networks with link correlations. In the presented approach the link travel times are characterized by lognormal distributions and the correlations between links are considered in the form of the correlation coefficient.…”

Section: Transit-based Routingmentioning

confidence: 99%

Intelligent route planning system based on interval computing

Chmiel

Skalna

Jędrusik

2018

Multimed Tools Appl

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We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

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The α-reliable path problem in stochastic road networks with link correlations: A moment-matching-based path finding algorithm

Cited by 46 publications

References 52 publications

Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China

Development of Multivariate Ordered Probit Model to Understand Household Vehicle Ownership Behavior in Xiaoshan District of Hangzhou, China

Introducing a Novel Method to Solve Shortest Path Problems Based on Structure of Network Using Genetic Algorithm

Intelligent route planning system based on interval computing

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