Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Civil and Environmental Engineering | en_US |
| dc.contributor.advisor | Chen, Anthony (CEE) | en_US |
| dc.contributor.advisor | Lam, H. K. William (CEE) | en_US |
| dc.contributor.advisor | Chung, Edward (EEE) | en_US |
| dc.creator | Kurmankhojayev, Daniyar | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13848 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Advancing link criticality analysis for large-scale road networks and bridge-centric transport networks : a network equilibrium approach | en_US |
| dcterms.abstract | Link criticality analysis for road networks is essential for mitigating and withstanding disruptive events. Frontier methods for link criticality analysis rely heavily on equilibrium traffic assignment (TA) models, which provide consistent network traffic flows and travel costs. These methods can account for network connectivity, redundancy, travel demand, individual travel choices, and congestion effects induced by traveler interactions. Conceptually, these methods define link criticalities in terms of their functional importance for normal network operations, as indicated by equilibrium TA model. Conventional methods assess link criticalities using a full-scan approach, which entails sequentially deactivating each link, solving a TA problem for each network modification, and subsequently reactivating the link before proceeding to the next one. While feasible for small networks, this approach is impractical for large-scale networks. Most studies have focused on the computational aspects of the problem, aiming either to approximate or bypass the full-scan process. In doing so, these studies often neglected the need for behaviorally plausible TA models for effective link criticality analysis. This oversight highlights the motivation for this thesis. | en_US |
| dcterms.abstract | It is well known that travelers often have incomplete knowledge of network conditions, which affects their individual route choices, and that they may switch modes of travel, change their departure times, or forego trips altogether in response to congestion. Ignoring these factors in TA models can lead to traffic flow patterns and network efficiency measures that do not adequately represent reality. Consequently, link criticality analysis methods based on TA models without these considerations may provide a poor representation of actual link criticalities. Nonetheless, most studies have assessed network link criticalities using user equilibrium TA models with fixed demand (UE-FD or UE), which is known to assume that travelers have perfect knowledge of network conditions, always use the least-cost routes, and never change their intension to travel, departure time, and mode of travel. To bridge this gap, this thesis advances an efficient link criticality analysis method by adopting stochastic user equilibrium (SUE) TA model with elastic demand (ED). In contrast to UE, SUE relaxes the assumption of perfect knowledge of network state and assigns traffic flows across all considered routes rather than just the least-cost routes, while ED adjusts travel demand based on congestion levels. The experiments show that the advanced method can prevent overestimating the criticality of links on least-cost routes, which is common for UE-based methods. Using real-world networks, it demonstrates that this method is applicable to large-scale road networks and consistent with the full-scan methods. | en_US |
| dcterms.abstract | Route similarity issues, stemming from route overlaps, are common in road networks. These issues not only substantially distort route choice probabilities, making similar routes less attractive from a travel cost perspective, but also impact origin-destination (O-D) travel demands. This can dramatically change total travel demand, O-D demand patterns, network flow patterns, travel costs, and, hence, link criticalities. Although the concept of route similarity has been well-explored within the context of SUE TA, its application in link criticality analysis remains limited. To address this research gap, this thesis adopts the cross-nested logit (CNL) SUE model with ED, which can flexibly capture the effects of route similarity on both disaggregate (or individuals') route choices and aggregate travel demand. It then incorporates this model into the selected link criticality analysis method. The results of the experiments demonstrate that the criticalities of links belonging to similar routes can be considerably overestimated if route similarity is not considered. | en_US |
| dcterms.abstract | Bridges play a vital role in road networks that are divided by obstacles like rivers and valleys. They connect different parts of the network and are essential for reaching certain destinations. Due to their limited number, they often become traffic bottlenecks, disproportionately affecting travel costs, and overall network performance. It has been recognized that bridges greatly influence route choice behavior, with travelers typically selecting bridges first and then deciding on the connecting routes. Despite this recognition, the functional importance of bridges has not been sufficiently emphasized in network equilibrium and link criticality analysis contexts. To address this research gap, this thesis introduces the concept of bridge-centric transport networks and develops a joint bridge-route choice model to better reflect how travelers first select bridges and then decide on the connecting routes. Then, it develops a network equilibrium model that encapsulates the joint bridge-route choice model along with a customized route-based solution algorithm, which consists of a bridge-centric choice set generation method and a route equilibration method. Finally, it applies the developed methodology to link criticality analysis. The results of the experiments demonstrate that network equilibrium models can produce substantially different traffic flow patterns and link criticality values, depending on whether they account for the importance of bridges in route choice. They also suggest that link criticality analysis methods based on traditional models may greatly underestimate the criticality of bridges. | en_US |
| dcterms.abstract | In summary, this thesis advances link criticality analysis by integrating nuanced equilibrium TA models that account for travelers’ imperfect perception of network conditions, their responses to congestion, and route similarity issues. Emphasizing the crucial role of bridges in the route choice process, it also develops a bridge-centric framework, which includes a joint bridge-route choice model, a network equilibrium model, and a customized route-based algorithm, and integrates it into the selected link criticality analysis method. The properties of the resulting methods are thoroughly investigated, and their validity and applicability to large-scale networks are demonstrated using real-world networks. | en_US |
| dcterms.extent | xxii, 193 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2025 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.accessRights | open access | en_US |
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