Personal vehicles are most widely used in the UAE since their number increased from 13.329 in 2015 to 13.594 in 2016 (“Annual statistical report 2014 – 2016,” 2017) compared to the public transportation fleet comprised of 1512 buses in 2016. Figure 1 contains data on bus ridership per purpose and provides background information regarding the economic and social value of bus services.
As for the level of accidents, personal vehicles take the first position (Figure 2) (“Bus transportation,” n.d; “RTA extends operation,” 2017; Shahbandari, 2015; Worku, 2013). Some other transportation types are studied as well (Holmgren, 2013; Kemp & Stephani, 2013; Tirachini, 2013; Qiu, Li, Zhang, Zhang, & Xie, 2015; Wirasinghe et al., 2013).
The data on accidents is employed to research and formulate recommendations for encouraging public transportation use among affluent Abu Dhabi residents. Since the rate of bus accidents is significantly low compared to private automobiles, this information may be used to address users’ safety concerns. Additionally, the data provided on Figures 4 and 5 − Benefits of the proposed Nol card system − is used to identify advantages associated with the electronic ticketing system, which can potentially attract the targeted population to public transportation services.
The study conducted by Road Safety UAE shows the levels of road safety monitoring based on the answers of respondents (Table 1) and implies that sustainable development is the key goal in the transportation of the UAE (Ahmed, 2017; Anderson, 2014; Asimet al. 2014; El-Bana, Selim, & Taleb, 2015; Gentile & Noekel, 2016; Goodwin & Van Dende, 2013; Kang, Khodadadifard, & Afandizadeh, 2017).
Table 1: UAE road safety monitoring (“Statistics,” 2017).
The comparison of frequency percentages represented in the table above is performed by using the Pearson correlation coefficient, which allows detecting attitude patterns among respondents in different periods. The information is used to address such specific study objectives as the specification of factors that need to be considered while implementing the project of interest, etc. Additionally, in the study the attention is paid to the target population, affluent Abu Dhabi citizens, focusing on their income, preferences, etc.
According to the estimates of the Household Income and Expenditure Survey (HIES), the average salary for people with high-income ranges from 40,000 to 200,000 AED (“About HIES,” 2013). In general, various industries show an increase in salary levels. For example, Figure 5 represents the accounting and finance employee survey highlights for UAE employees.
Reviewing Figures 6 and 7, one may observe the average spending on transportation and other necessary items in Abu Dhabi.
Geographic information systems (GIS) data is used to identify the required length and other specifics of a dedicated bus lane and determine how road traffic is regulated to ensure the effectiveness of a new system (Daraio et al., 2016; deOña, de Oña, Eboli, & Mazzulla, 2013; Hassan, Hawas, & Ahmed, 2013; Parahoo, Harvey, & Radi, 2014; Redman, Friman, Gärling, & Hartig, 2013; Xenias & Whitmarsh, 2013). The quantitative method with the use of the linear regression allows examining the links between distinct covariates (e.g., lane length, Nol card system use, etc.) and a numeric outcome (i.e., cost efficiency and social effectiveness of transportation service).
The comparison of data on Dubai’s several existing dedicated bus lanes (e.g., located on Al Ghubaiba road, “about 320 meter from Al Mina Intersection up to Street 12”) and Nol card system functions helps reveal the relationship between suggested covariates and expected outcomes (Government of Dubai, 2017, para. 1; “A better efficiency”, 2014; Bajracharya, 2016; Chakravarty & Qamhaieh, 2015; Guo, 2013; Henderson, 2014; Innocenti, Lattarulo, & Pazienza, 2013; Khoo, Teoh, & Meng, 2014; Misra, 2016; Shahbandari, 2015; Zambonini & Zafar, 2014). Considering the specific Abu Dhabi road issues and the purpose of a dedicated bus lane, it will be important to analyze more data (Ma, Head, & Feng, 2014; Sbia, Shahbaz, & Hamdi, 2014; Şimşekoğlu, Nordfjærn, & Rundmo, 2015). After data analysis phase, it is expected to provide valuable recommendations for further implementation of the proposed innovations.
Table 2: Multi-stakeholder matrix.
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