Resilience-based Operational Analytics of Transportation Infrastructure: A Data-driven Approach for Smart Cities
| dc.contributor.author | Khaghani, Farnaz | en |
| dc.contributor.committeechair | Jazizadeh, Farrokh | en |
| dc.contributor.committeemember | Fox, Edward A. | en |
| dc.contributor.committeemember | de la Garza, Jesus M. | en |
| dc.contributor.committeemember | Garvin, Michael J. | en |
| dc.contributor.committeemember | Rakha, Hesham A. | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2020-07-02T08:00:33Z | en |
| dc.date.available | 2020-07-02T08:00:33Z | en |
| dc.date.issued | 2020-07-01 | en |
| dc.description.abstract | Studying recurrent mobility perturbations, such as traffic congestions, is a major concern of engineers, planners, and authorities as they not only bring about delay and inconvenience but also have consequent negative impacts like greenhouse gas emission, increase in fuel consumption, or safety issues. In this dissertation, we proposed using the resilience concept, which has been commonly used for assessing the impact of extreme events and disturbances on the transportation system, for high-probability low impact (HPLI) events to (a) provide a performance assessment framework for transportation systems' response to traffic congestions, (b) investigate the role of transit modes in the resilience of urban roadways to congestion, and (c) study the impact of network topology on the resilience of roadways functionality performance. We proposed a multi-dimensional approach to characterize the resilience of urban transportation roadways for recurrent congestions. The resilience concept could provide an effective benchmark for comparative performance and identifying the behavior of the system in the discharging process in congestion. To this end, we used a Data Envelopment Analysis (DEA) approach to integrate multiple resilience-oriented attributes to estimate the efficiency (resilience) of the frontier in roadways. Our results from an empirical study on California highways through the PeMS data have shown the potential of the multi-dimensional approach in increasing information gain and differentiating between the severity of congestion across a transportation network. Leveraging this resilience-based characterization of recurrent disruptions, in the second study, we investigated the role of multi-modal resourcefulness of urban transportation systems, in terms of diversity and equity, on the resilience of roadways to daily-based congestions. We looked at the physical infrastructure availability and distribution (i.e. diversity) and accessibility and coverage to capture socio-economic factors (i.e. equity) to more comprehensively understand the role of resourcefulness in resilience. We conducted this investigation by using a GPS dataset of taxi trips in the Washington DC metropolitan area in 2017. Our results demonstrated the strong correlation of trips' resilience with transportation equity and to a lesser extent with transportation diversity. Furthermore, we learned the impact of equity and diversity can mostly be seen at the recovery stage of resilience. In the third study, we looked at another aspect of transportation supply in urban areas, spatial configuration, and topology. The goal of this study was to investigate the role of network topology and configuration on resilience to congestion. We used OSMnx, a toolkit for street network analysis based on the data from OpenStreetMap, to model and analyze the urban roadways network configurations. We further employed a multidimensional visualization strategy using radar charts to compare the topology of street networks on a single graphic. Leveraging the geometric descriptors of radar charts, we used the compactness and Jaccard Index to quantitatively compare the topology profiles. We use the same taxi trips dataset used in the second study to characterize resilience and identify the correlation with network topology. The results indicated a strong correlation between resilience and betweenness centrality, diameter, and Page Rank among other features of a transportation network. We further looked at the capacity of roadways as a common cause for the strong correlation between network features and resilience. We found that the strong correlation of link-related features such as diameter could be due to their role in capacity and have a common cause with resilience. | en |
| dc.description.abstractgeneral | Transportation infrastructure systems are among the most fundamental facilities and systems in urban areas due to the role they play in mobility, economy, and environmental sustainability. Due to this importance, it is crucial to ensure their resilience to regular disruptions such as traffic congestions as a priority for engineers and policymakers. The resilience of transportation systems has often been studied when disasters or extreme events occur. However, minor disturbances such as everyday operational traffic situations can also play an important part in reducing the efficiency of transportation systems and should be considered in the overall resilience of the systems. Current literature does not consider traffic performance from the lens of resilience despite its importance in evaluating the overall performance of roads. This research addresses this gap by proposing to leverage the concept of resilience for evaluation of roadways performance and identifying the role of urban characteristics in the enhancement of resilience. We first characterized resilience considering the performance of the roadways over time, ranging from the occurrence of disruptions to the time point when the system performance returns to a stable state. Through a case study on some of the major highways in the Los Angeles metropolitan area and by leveraging the data from the Performance Measurement System (PeMS), we have investigated how accounting for a proposed multi-dimensional approach for quantification of resilience could add value to the process of road network performance assessment and the corresponding decision-making. In the second and third parts of this dissertation, we looked at the urban infrastructure elements and how they affect resilience to regular disruptive congestion events. Specifically, in the second study, we focused on alternative transit modes such as bus, metro, or bike presence in the urban areas. We utilized diversity and equity concepts for assessing the opportunities they provide for people as alternative mobility modes. The proposed metrics not only capture the physical attributes of the multi-modal transportation systems (i.e. availability and distribution of transit modes in urban areas) but also consider the socio-economic factors (i.e. the number of people that could potentially use the transit mode). In the third study, we investigated how urban road networks' form and topology (i.e., the structure of roadway networks) could affect its resilience to recurrent congestions. We presented our findings as a case study in the Washington DC area. Results indicated a strong correlation between resilience and resourcefulness as well as topology features. The findings allow decision-makers to make more informed design and operational decisions and better incorporate the urban characteristics during the priority setting process. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:26505 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/99206 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Resilience | en |
| dc.subject | Resourcefulness | en |
| dc.subject | Diversity | en |
| dc.subject | Equity | en |
| dc.subject | Network topology | en |
| dc.subject | Taxicab GPS data | en |
| dc.subject | Data Envelopment Analysis | en |
| dc.title | Resilience-based Operational Analytics of Transportation Infrastructure: A Data-driven Approach for Smart Cities | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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