GRM 2010 GRM 2011

Abstract Details

 
AUTHOR NAME
 
Family Name:
Abula
 
First Name:
Baiherula
 
ABSTRACT OF PAPER
 
Title of Paper:
Evaluation of Dubai’s Road Network Resilience against Intersection and Interchange Disruptions using a Graph Theory-Based Approach
 
Paper Proposal Text :
Introduction and Background:
A transportation network is to an urban area as the circulatory system is to a human body, because as the circulatory system in the human body transports oxygen and nutrients to numerous cells in the body to ensure the health of various organs, the transportation system transfers people, goods and services to ensure the health of the economy between the different parts of the city. In addition to facilitating people’s everyday movements and transportation of goods, the road system functions as a life-line system for rescuing people and goods during emergencies and plays a vital role in repairing and restoring other infrastructure systems when their functionalities are disrupted by sending the necessary repair team to the location (Mattsson & Jenelius, 2015). Achieving “Smartness” in cities, requires, primarily, the strengthening the resilience of its life-line infrastructure, i.e. their capacity to preserve or restore their modus operandi against the effects of any unexpected events that may challenge their operational performance and continuity.
Dubai is one of the seven emirates in the United Arab Emirates (UAE) federation. It is the most populous city in the UAE (2,174,000 by August, 2013)(Abula, 2014). It is the Middle East’s capital of commerce and home to the largest number of foreign businesses in the region (Krane, 2009). This economic prosperity has been highly dependent upon a well-functioning transportation system in the city, as at the heart of all these economic activity is trade, which requires transportation of goods or people to work, shopping, tourist sites and meeting locations (Savage & Schupp, 1997). As overwhelming majority of the trips in Dubai are conducted on roads, identifying vulnerabilities and enhancing the resilience within its road network is of great significance for the enhancement of resilience of the city’s transportation network in the face of both human-made (e.g. frequent accidents) and natural (e.g. occasional rain storms) disruptions. This paper shares this objective and the purpose of the proposed study is to evaluate the resilience of Dubai’s road networks by quantifying it against disruptions impacting intersections/interchanges using a novel graph-theory based approach.

Proposed Methodology:
While this study has obtained inspirations of varying degrees from many literature sources reviewed, to be more specific, below four papers serve the major theoretical foundation for the methodology proposed in this study:
• Demšar, Špatenková, and Virrantaus (2008)
• Leu, Abbass, and Curtis (2010)
• Ip and Wang (2011)
• King, Shalaby, and Eng (2016)
Most existing literature utilizing metrics of graph theory for analysis and quantification of the resilience of transportation networks do not account for the varying importance of nodes/links in the network that is not related to their connectivity or centrality. Instead, they consider the amount of traffic flow that traverses through them. However, as a study by Zhou et al. (Zhou & Sisiopiku, 1997) suggests, volume capacity ratio is an important indicator of the accident rates on the roads, which means a true indicator of vulnerability is not only the traffic flow volume, but also the volume capacity ratio on the nodes. This study proposes a method which combines the graph theory measures with v/c of the nodes on the major road networks in order to have a more realistic assessment of the road resilience.
This study represents the network of main roads in the city of Dubai as an undirected weighted graph, with the nodes being the intersections/interchanges and border limits, the edges (links) being the segments of roads running between the nodes, and weights being the lengths of the edges, as suggested by Crucitti et al (2006). The road networks are represented using Gephi and Matlab for the required analysis.
The methodology adopted in this study follows six main steps: (1) The data used for the project has been acquired from two major sources, Dubai Road Transport Authority (RTA) and Google Traffic respectively for the construction of the road network and estimation of traffic flow situation on the network; (2) The graph theoretic and transportation metrics used for the analysis will be identified from the literature in order to model the resilience of the road network from infrastructure and traffic demand points of view ; (3) The significance of each node in the network will be quantified based on their centrality measures and v/c; (4) The overall resilience of the network is quantified by aggregation of the node significances; (5) Sensitivity analysis using different scenarios is conducted in order to do adjustments to the resilience metrics used for the model; (6) Final methodology is selected by adjusting the number of independent variables as well as their coefficients based on the results of sensitivity analysis.
Originality and Advantages of Proposed Methodology
Due to their planar nature, urban road systems could be conveniently represented as complex networks. This paper proposes a graph-theory based approach for the assessment of resilience of Dubai’s road network, to the best of the authors’ knowledge, no such study of the Dubai road networks’ resilience has previously been conducted. The resilience analysis of the road network using graph theory therefore provides a basis for future extensive work that can be conducted to assess the capability of the transportation networks to handle potential disasters and what measures can be taken in advance of these disasters so that the networks maintains their functionality. In this study, the physical and logical layers of the road network, as defined by Kurant and Thiran (2006), are both accounted for. A metric for quantification of the resilience of the road network is developed taking into account above both layers of the road network. Another advantage of this study is its effective usage of publicly available big data generated by Google traffic for the estimation of the v/c.
Based on the results of the study, some practical suggestions for enhancing the resilience of the road networks in Dubai will be presented, which will be followed by the discussions about the limitations of the proposed methodology and possible ways to overcome it by future work.

Works Cited

1. Abula, B. (2014). Application of Real Options Method to the Location of Light Rail Stations. Masdar Institute of Science and Technology.
2. Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3), 036125.
3. Demšar, U., Špatenková, O., & Virrantaus, K. (2008). Identifying critical locations in a spatial network with graph theory. Transactions in GIS, 12(1), 61-82.
4. Ip, W., & Wang, D. (2011). Resilience and friability of transportation networks: evaluation, analysis and optimization. IEEE Systems Journal, 5(2), 189-198.
5. King, D., Shalaby, A., & Eng, P. (2016). Performance Metrics and Analysis of Transit Network Resilience in Toronto. Paper presented at the Transportation Research Board 95th Annual Meeting.
6. Krane, J. (2009). Dubai: The story of the world's fastest city: Atlantic Books Ltd.
7. Kurant, M., & Thiran, P. (2006). Layered complex networks. Physical review letters, 96(13), 138701.
8. Leu, G., Abbass, H., & Curtis, N. (2010). Resilience of ground transportation networks: a case study on Melbourne.
9. Mattsson, L.-G., & Jenelius, E. (2015). Vulnerability and resilience of transport systems–a discussion of recent research. Transportation Research Part A: Policy and Practice, 81, 16-34.
10. Savage, I., & Schupp, A. (1997). Evaluating transit subsidies in Chicago: Transportation Research Board.
11. Zhou, M., & Sisiopiku, V. (1997). Relationship between volume-to-capacity ratios and accident rates. Transportation Research Record: Journal of the Transportation Research Board(1581), 47-52.

 
 
 

WITH THE GENEROUS SUPPORT OF