Finding most reliable paths on networks with correlated and shifted log–normal travel times

Srinivasan, Karthik K.; Prakash, A. Arun; Seshadri, Ravi

doi:10.1016/j.trb.2013.10.011

Cited by 104 publications

(58 citation statements)

References 32 publications

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“…Travel times were thus viewed as random variables, and followed a probability distribution, for example an exponential, normal, or lognormal distribution [30]. Algorithms have been proposed to find reliable shortest paths or most reliable paths between an origin-destination pair by assuming travel times following a normal or lognormal distribution [9,31]. However, these cannot be easily applied to rescue vehicle routing in the case of multiple destinations (affected areas).…”

Section: Literature Reviewmentioning

confidence: 99%

Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

2018

IJGI

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Abstract:The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and tabu search (TS) was designed to solve the model. An experiment optimizing rescue routing plans under a real urban storm event, was carried out to validate the proposed model. The experimental results showed how our approach can improve rescue efficiency with high travel time reliability.

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Section: Literature Reviewmentioning

confidence: 99%

Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

2018

IJGI

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“…Typically, the covariance matrix is calibrated assuming only adjacent (or second-order, etc.) link travel time correlations (Srinivasan et al, 2014).…”

Section: 431generalized Markov Chain (Gmc) Approachmentioning

confidence: 99%

“…Two MGF models are implemented based on the path TTD estimation approach used by Srinivasan et al (2014) and the MGF method described in Section 7.4.3. These two models are associated with different assumptions about the link TTDs, normal (MGF_CN), and lognormal (MGF_CLN).…”

Section: Performance Comparisonmentioning

confidence: 99%

“…7:00 = 7:00-7:30 am). Four models are presented: convolution, GMC_GMMS_NC , GMC_GMMS_CLN and MGF_CLN (Srinivasan et al, 2014). The MC_GMMS model is not shown as it was not feasible to estimate the distribution for trips with more than 9 links due to computational reasons which underlies one of the major limitations.…”

Section: Trip Distancementioning

confidence: 99%

“…If two distributions have the same MGFs, then they have identical distributions. Srinivasan et al (2014) used a MGF based approach to estimate the trip TTD as the sum of the correlated link TTDs assuming they have a unimodal distribution. However, the unimodal assumption is not always appropriate.…”

Section: Moment Generating Function (Mgf) Algorithmmentioning

confidence: 99%

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Modelling reliability and distribution of travel times in transit

Ma¹

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The performance of Transit Travel Time Reliability (TTR) influences service attractiveness, operating costs and system efficiency. Transit agencies have spent considerable effort on implementation of strategies related to advanced technologies capable of improving service reliability. Survey studies have shown that travelers tend to value a reduction in unreliability at least as important as a decrease in the average travel time. The increasing availability of data from automatic collection systems (e.g. automatic vehicle location, automatic fare collection, and etc.) provides opportunities in addressing transit TTR challenges. While most past studies estimate TTR for impact assessment of strategic and operational instruments, this research aims at developing generic models for TTR prediction that can fulfil different transit stakeholders' requirements (e.g. operators, unreliability causes identification; passengers, trip and departure planning). Three main issues are addressed, namely TTR quantification, TTR modelling and Travel Time Distribution (TTD) estimation. A unique integrated data warehouse was established for case studies of this research using different sources of data across six months of a year in Southeast Queensland area, Australia.For TTR quantification, a set of TTR measures from the perspective of passengers using the operational AVL data was proposed, considering different perceptions of TTR under different traffic states. The results show that the proposed measure can provide consistent TTR assessments with high-level of details, while the conventional TTR measures may give inconsistent assessments. For TTR modelling, the underlying determinants of travel time unreliability were identified and quantified on links of different road types using Seemingly Unrelated Regression Equations (SURE) estimation to account for the cross-equation correlations across regression models caused by unobserved heterogeneity. Targeted strategies can be introduced to improve TTR under different scenarios. For TTD estimation, a novel evaluation approach was developed to assess the most appropriate probability distributions for travel time components (link running times and stop dwell times). The Gaussian Mixture Models (GMM) distribution was assessed to be superior to its alternatives, in terms of fitting accuracy, robustness and explanatory power. The correlation structures of travel time components were explored using both a global and a local correlation measures. On these basis, a generalized Markov chain model was proposed to estimate the trip TTDs for arbitrary originationdestination pairs at arbitrary times given the individual link TTDs, by considering their spatiotemporal correlations. The proposed approach is generalizable and computationally more efficient, while it provides a comparable performance with reported models in literature.A major contribution of the research is the establishment of a generic TTD estimation methodology that can be applied for a comprehensive analysis and prediction of...

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Efficient solution algorithm for finding spatially dependent reliable shortest path in road networks

Chen

Lam

2016

J of Advced Transportation

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Summary Travel times are generally stochastic and spatially correlated in congested road networks. However, very few existing route guidance systems (RGS) can provide reliable guidance services to aid travellers planning their trips with taking account explicitly travel time reliability constraint. This study aims to develop such a RGS with particular consideration of travellers' concern on travel time reliability in congested road networks with uncertainty. In this study, the spatially dependent reliable shortest path problem (SD‐RSPP) is formulated as a multi‐criteria shortest path‐finding problem in road networks with correlated link travel times. Three effective dominance conditions are established for links with different levels of travel time correlations. An efficient algorithm is proposed to solve SD‐RSPP by adaptively using three established dominance conditions. The complexities of road networks in reality are also explicitly considered. To demonstrate the applicability of proposed algorithm, a comprehensive case study is carried out in Hong Kong. The results of case study show that the proposed solution algorithm is robust to take account of travellers' multiple routing criteria. Computational results demonstrate that the proposed solution algorithm can determine the reliable shortest path on real‐time basis for large‐scale road networks. Copyright © 2016 John Wiley & Sons, Ltd.

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Finding most reliable paths on networks with correlated and shifted log–normal travel times

Cited by 104 publications

References 32 publications

Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

Modelling reliability and distribution of travel times in transit

Efficient solution algorithm for finding spatially dependent reliable shortest path in road networks

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