عنوان مقاله [English]
Intelligent transportation systems can be effective in achieving all the goals of the Tehran city comprehensive transportation and urban traffic plan. In order to obtain the most out of this potential, it is necessary to provide a mathematical methodology to improve the productivity and efficiency of the systems. This research was focused on the evaluation of productivity and efficiency of intelligent transportation systems in Tehran City. The methodology of this paper is mathematical programming, using Multi Criteria Decision Making (MCDM) methods. Primarily, the main components of intelligent transportation systems in Tehran city, were identified and the system’s costs and benefits were calculated through literature review and using available resources in four aspects of safety, performance, profitability and the environment. The collected data was presented in an expected index rate table after utilizing the Intelligent Transportation in Tehran city. The costs and benefits were then coded in the GAMS software through the Data Envelopment Analysis Method (DEA). Results have shown that Ramp Metering System and Signal Priority intelligent management systems have the highest coefficient factor of productivity and advanced traveler information system, intelligent parking management system, intelligent signaling, variable message signs have shown relevantly high coefficient factor of productivity. Intelligent transportation systems can be identified as a composition of tools, features and expertise such as traffic engineering concepts, software, hardware and telecommunication technologies, which are coordinated and integrated to improve efficiency and safety of transportation systems. The efficiency of the intelligent transportation systems is a function of information and communication technologies, which are going through rapid changes. The importance of the intelligent transportation systems management is more significant due these changes through time. Therefore, the intelligent transportation systems planning should be updated and practical. This requires continuous review and evaluation of new proposed needs and strategies and solutions. Details should be considered closely in the planning process of the intelligent transportation systems, so the future potential improvement opportunities are guaranteed. Future intelligent transportation planning needs such as geographic and systematic requirements, should be studied in order to be responsive to forthcoming traffic needs and also considering potential improvement capacities. For this purpose, the intelligent transportation systems productivity will improve with defining functional indicators about the productivity of intelligent transportation systems and measuring them in order to evaluate the function and efficiency utilizing DEA method. Due to the efficiency importance in the ITS activities development, and because of these system’s equipment, operation and maintenance high expenses utilizing this method could be useful in choosing and prioritizing these systems activities. In this research, to keep the categorized decision maker units, the income efficiency of the units are calculated using sustainability network method. One of the advantages of the proposed method is that, the sensitivity analysis about the income efficiency could help decision makers to figure out in what extent the inputs or outputs data of the units under evaluation should be changed in order to achieve the highest profit and productivity. The efficiency and productivity of ITS, are dependents of information and communication technologies (ICT) which are changing fast. Therefore, intelligent transportation systems planning become more important than ever, over the time. These systems should be executive and updated and for this purpose, they should be checked out continuously for estimating recent needs and evaluating new strategies and solutions. The system also must be checked out after executing in the region under evaluation and gaining enough experience. In a research in 2012, an efficiency evaluation thesis for intelligent management system of traffic based on DEA models, was propounded by Wei Z. A cost efficiency model is developed in this research by DEA. The basic information was gathered according to empirical analysis of intelligent transportation systems, implemented projects in Beijing, between 2000 and 2010. According to the rapid extension and development of the cities and urbanization, the proposed results in this condition and due to travel demand increase, urban traffic management is faced to many challenges. So that, the operational planning improvement would be inevitable for efficiency improvement and development of public transportation systems productivity. Therefore, a strong structure of intelligent transportation system planning could be a solution to optimizing the urban development, urban transportation modes combination, the guarantee of public transportation movement prioritizing and finally a sustainable urban development achievement. After studying the transportation systems influences on safety issues, traffic operation, user’s satisfaction, environmental issues, transportation systems operation costs would be calculable in order to estimating the benefits of suggested activities for developing the intelligent transportation systems. Accordingly, the improvement amount of safety, efficiency, usefulness and environmental indicators of these activities will be calculated. In this research, productivity coefficients of suggested activities to develop the intelligent transportation systems will be determined by using data envelopment analysis (DEA) method and revenue model according to the suggestions of Wei Z. research.
Afandizadeh, Sh., Rahimi, A.M. (2009). Transportation Engineering: Planning and Design. Iran University of Science and Technology.
Cheng, H.Y., Huang, C.W., Hwang, J.N. (2012). Advanced Formation and Delivery of Traffic Information in Intelligent Transportation Systems, Expert Systems with Applications, 39, 8356–8368.
City of Sydney, City of Sydney Strategic Plan 2030: Final Consultation Draft, City of Sydney. (2008).(www.sydney2030.com.au).
Evaluation of Communication Alternatives for Intelligent Transportation Systems. (2010). South Carolina Department of Transportation.
Hashemi, M., Taherkhani, H. Saeidi Rezvani, H. Basirat, M. (2011). A Pattern for Mid-Term Program Development of Municipalities in Iran. Shahr Negar Journal, 55 (15).
Jianwei, H., Zhenxiang, Z., Zhiheng, L. (2010). Benefit Evaluation Framework of Intelligent Transportation Systems, Journal of Transportation System Engineering and Information Technology, 10(1).
Lake Tahoe Transportation Planning Overall Work Program. (2013). Tahoe Metropolitan Planning Organization, Federal Highway Administration.
Lee, D. B. (1999). Benefit-Cost Evaluation of ITS Projects: Seattle WSDOT Web Site (SE-26), paper prepared for the FHWA/FTA Joint Program Office, USDOT.
Nam, D.H., Drew, D.R. (1999). Automatic Measurement of Trace Variables for Intelligent Transportation Systems Applications, Transportation Research Part B, 33, 437-457.
N. Fries, R., Reisi Gahrooei, M., Chowdhury, M., & J. Conway, A. (2012). Meeting Privacy Challenges While Advancing Intelligent Transportation Systems, Transportation Research Part C, 25, 34–45.
Quezada. L.E. (2009). Method for Identifying Strategic Objectives in Strategy Maps, Int. J. Production Economics, 122, 492–500.
Saeidnia, A. (2003). Review of Detailed and Comprehensive Design’s Services. Ministry of Housing and Urban Development of Iran.
Seyed Hosseini, S.M. Transportation Engineering Planning and Material Handling Analysis. Iran University of Science and Technology.
Siadat Mousavi, M. (2010). Fundamentals of Intelligent Transportation Systems Planning. Transportation and Traffic Assistance of Tehran Municipality.
State Planning and Research Work Program FFY 2013. (2013). Oklahoma Department of Transportation, US Department of Transportation Federal Highway Administration.
Taherkhani, H., Saeidi Rezvani, H., Basirat, M. (2012). Structure and Achievements of Mid-Term operating plan of Tehran Municipality. Monthly Journal of Municipalities, 107 (112).
Transportation and Traffic Comprehensive Design Studies. (2008). Transportation and Traffic Assistance of Tehran Municipality.
Wei, Z., Zhao, P., Shulin, A. (2012). Efficiency Evaluation of Beijing Intelligent Traffic Management System Based on super-DEA, Journal of Transportation Systems Engineering and Information Technology, 12(3).
Xiong, Z., Sheng, H., Rong, W., Cooper, D. (2012). Intelligent Transportation Systems for Smart Cities: A Progress Review, Sci China Inf Sci, 55, doi: 10.1007/s11432-012-4725-1.
Zhu, J. (2002). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets and DEA Excel Solver, Norwell, MA: Kluwer Academic Publishers Group.