ORIGINAL_ARTICLE
A Maturity Model for Digital Transformation in Transportation Activities
Today, one of the important frameworks in understanding and recognizing digital transformation in all activities is the digital transformation maturity model. Understanding the dimensions and stages of maturity is important for transportation decision makers because in this way they can make appropriate management decisions by understanding the position of their organization in the maturity of digital transformation. The main purpose of this study is to present the maturity model of digital transformation capability, determine its stages, and investigate the use of Intelligent Transportation Systems and the maturity of transportation-related activities. In this paper, meta-synthesis is used to study the different models and stages of digital transformation maturity in various scientific databases on the Internet and provide a comprehensive summary of the dimensions and stages of maturity. After evaluation, 30 transportation companies in Tehran and their main activities were selected. By analysing the dimensions and stages of maturity in previous papers, the maturity model presented in this paper includes five stages and 10 dimensions in transportation area. These dimensions include digital management, information technology, manpower, operations and processes, culture, organizational structure, innovation and change, new strategies, intelligent products and services and customer orientation. Therefore, the maturity of 14 transportation activities was measured. The results showed that most transportation activities are at level three of the maturity model. Also, dimensions that scored the most are, respectively, digital process and operations of transportation, digital innovation in transportation, structure and governance, and digital management in transportation.
http://www.ijte.ir/article_135993_e9b061908cad30a15ffa209883838318.pdf
2021-07-01
415
438
10.22119/ijte.2021.261913.1551
digital transformation
Transportation
Meta-Synthesis
Maturity model
Intelligent Transportation Systems
Elham
Asadamraji
e.asadamraji@gmail.com
1
Department of Information Technology Management, Tehran North Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Ali
Rajabzadeh GHatari
alirajabzadeh@modares.ac.ir
2
Industrial Management Department, Faculty of Management, Tarbiat Modares University, Tehran, Iran
AUTHOR
Maryam
Shoar
m_shoar@iau-tnb.ac.ir
3
Department of Industrial Management, Tehran North Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
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56
ORIGINAL_ARTICLE
Identifying the factors affecting road accidents and providing multi-criteria hybrid decision-making methods for ranking hazardous points
Traffic accidents and their consequences are among the major issues that need to be seriously addressed in today’s world. In this study, the prioritization of hazardous points on the roads is discussed, using multi-criteria decision-making (MCDM) techniques considering different natural and environmental criteria affecting road accidents. The Neyshabour-Sabzevar and vice versa road axes in Khorasan province, Iran, are considered as a case study for the implementation of the proposed method. Initially, 20 criteria were identified in 4 different categories to prioritize the hazardous points using the literature review and the experts’ opinion. In this paper, the MDL (Modified Digital Logic) and AHP (Analytical Hierarchy Process) methods are used to determine the criteria’s weights. By combining these techniques, four hybrid methods MDL-TOPSIS (Technique for order preference by similarity to an ideal solution), MDL-VIKOR (Vlse Kriterijumska Optimizacija I Kompromisno Resenje), AHP-TOPSIS, and AHP-VIKOR are obtained to prioritize the mentioned points, each producing different results. Two models were used to obtain the final ranking. In the first model, the results of these four methods are integrated using the COPELAND method. In the second model, the entropy method (Emerging Network To Reduce Orwellian Potency Yield) is used to modify the weight of the criteria. The innovation of the paper is presenting a new hybrid MCDM method that is used to prioritize hazardous points. Results showed that using the entropy method for modifying the weight of the criteria causes the methods to produce the same results. Moreover, results show that the number of deadly injured casualty of an accident is the most important criterion. Additionally, Zafaranieh residential area gained the highest priority.
http://www.ijte.ir/article_135994_0ba593ac9ae679933e5cb81225dc72f7.pdf
2021-07-01
439
458
10.22119/ijte.2021.260481.1548
Hazardous Points
MCDM
ENTROPY Method
Copeland method
Hybrid ranking method
Mohammad Ali
Beheshtinia
beheshtinia@semnan.ac.ir
1
Department of Industrial Engineering, University of Semnan
LEAD_AUTHOR
Shakiba
Sayadinia
sh.sayadinia@semnan.ac.ir
2
Master of Science in Business Administration, Semnan University, Semnan, Iran
AUTHOR
Hengameh
Bargebid
hbargebid@yahoo.com
3
M.S. in Industrial Engineering, Semnan University, Semnan, Iran
AUTHOR
- Shafabakhsh, G.A., F. Fathi, and A. Zayerzadeh, Prioritization of eventful roads correction using artificial neural networks. Journal of modeling in engineering, 2010. 8(20): p. 71-81.
1
- Dehghan-Manshadi, B., et al., A novel method for materials selection in mechanical design: combination of non-linear normalization and a modified digital logic method. Materials & design, 2007. 28(1): p. 8-15.
2
- Beheshtinia, M.A. and V. Nemati-Abozar, A Novel Hybrid Fuzzy Multi-Criteria Decision-Making Model for Supplier Selection Problem (A Case Study in Advertising industry). Journal of Industrial and Systems Engineering, 2017. 9(4): p. 65-79.
3
- Beheshtinia, M.A. and S. Omidi, A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes, 2017. 46(8): p. 1386-1407.
4
- Sedady, F. and M.A. Beheshtinia, A novel MCDM model for prioritizing the renewable power plants’ construction. Management of Environmental Quality: An International Journal, 2019. 30(2): p. 383-399.
5
- Gangil, M. and M.k. Pardhan, Optimization the machining parameters by using VIKOR Method during EDM process of Titanium alloy. Materials Today: Proceedings, 2018. 5: p. 7486–7495.
6
- Geurts, K., et al., Identification and ranking of black spots: Sensitivity analysis. Transportation Research Record: Journal of the Transportation Research Board, 2004. 1897: p. 34-42.
7
- Geurts, K., et al., Ranking and selecting dangerous crash locations: Correcting for the number of passengers and Bayesian ranking plots. Journal of safety research, 2006. 37(1): p. 83-91.
8
- Elvik, R., Comparative analysis of techniques for identifying locations of hazardous roads. Transportation Research Record: Journal of the Transportation Research Board, 2008. 2083: p. 72-75.
9
- Montella, A., A comparative analysis of hotspot identification methods. Accident Analysis & Prevention, 2010. 42(2): p. 571-581.
10
- Reshma, E. and S.U. Sharif, Prioritization Of Accident Black Spots Using GIS. International Journal Of Emerging Technology And Advanced Engineering, 2012. 2(9): p. 117-122.
11
- Park, P.Y. and J. Young, Investigation of a supplementary tool to assist in the prioritization of emphasis areas in North American strategic highway safety plans. Accident Analysis & Prevention, 2012. 45: p. 392-405.
12
- Kwak, H.C. and S. Kho, Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data. Accident Analysis & Prevention, 2016. 88: p. 9-19.
13
- Zhang, G., et al., Traffic accidents involving fatigue driving and their extent of casualties. Accident Analysis & Prevention, 2016. 87: p. 34-42.
14
- Kountouriotis, G. and N. Merat, Leading to distraction: Driver distraction, lead car, and road environment. Accident Analysis & Prevention, 2016. 89: p. 22-30.
15
- Farajollahi, G. and M.R. Delavar, Assessing accident hotspots by using volunteered geographic information. Journal CleanWAS, 2017. 1(2): p. 14-17.
16
- Dereli, M.A. and S. Erdogan, A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transportation Research Part A, 2017. 103: p. 106-117.
17
- Hoe, L.W., et al., A Study on the Impact of Climate and Road Factors Towards Road Accidents in Malaysia with Analytic Hierarchy Process. American Journal of Environmental Policy and Management, 2018. 4(2): p. 54-59.
18
- Nenadić, D., Ranking dangerous sections of the road using the mcdm model. Decision Making: Applications in Management and Engineering, 2019. 2(1): p. 115-131.
19
- Tola, A.M. and A. Gebissa, Identifying Black Spot Accident Zones Using a Geographical Information System on Kombolcha-Dessie Road in Ethiopia. International Journal of Sciences: Basic and Applied Research (IJSBAR), 2019. 48(1): p. 66-79.
20
- Fernandez, J.J., et al., Driver's Road Accident Factor Prioritization using AHP in Relation to Mastery of Traffic Signs in the City of Manila. Transportation Research Procedia, 2020. 48: p. 1316-1324.
21
- Abdel-Basset, M., et al., A new hybrid multi-criteria decision-making approach for location selection of sustainable offshore wind energy stations: A case study. Journal of Cleaner Production, 2021. 280: p. 124462.
22
- Bakioglu, G. and A.O. Atahan, AHP integrated TOPSIS and VIKOR methods with Pythagorean fuzzy sets to prioritize risks in self-driving vehicles. Applied Soft Computing, 2021. 99: p. 106948.
23
- Sadeghi, A., E. Ayati, and M. Pirayesh Neghab, Identification and prioritization of hazardous road locations by segmentation and data envelopment analysis approach. PROMET-Traffic&Transportation, 2013. 25(2): p. 127-136.
24
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25
ORIGINAL_ARTICLE
Implementation of an Integrated Traffic System in Metropolitan Areas: A Case Study of Tehran, Iran
Each natural and human element plays an essential role in the formation the morphological structure of cities, through which transportation systems are the most important factor among human factors. An integrated transportation strategy is a sustainable transportation strategy aiming to reduce inconsistencies and divisions in the transportation service management system. As the largest metropolis in the country and the Middle East, Tehran is facing serious problems with its transportation system. However, sufficient studies have not been conducted to comprehensively identify structural barriers in the urban management system of the Tehran metropolis to implement an integrated transportation management system. The paper aims to identify structural barriers in the urban management system of Tehran and provide an overview of measures that can be taken to implement this approach in this metropolis. The used research method is the survey-analytical method and a questionnaire was employed to collect data. The identified factors have been classified through confirmatory factor analysis (CFA) and analytic hierarchy process (AHP) methods and research hypotheses have been tested by a regression method. The results show that there are no codified executive rules for the realization of urban transportation integrated management system in Tehran. Additionally, findings indicate that the necessary financial credits are not available to the municipality and there is no coordination between decision makers on the issue of handing over the management of the transportation system to the municipality. Based on results, Tehran municipality has the necessary potential to accept the responsibility of managing this system.
http://www.ijte.ir/article_135995_a2f21a57492970c9f1e648e4c8a79f74.pdf
2021-07-01
459
474
10.22119/ijte.2021.251540.1538
Integrated
Transportation
Regression
Codified Executive Rules
Management
Gholamreza
Shirazian
reza@shirazian.org
1
Transportation Planning, University of North, Amol, Iran
AUTHOR
Mohammad Reza
Eskandari
eskandarimohammadreza833@gmail.com
2
Transportation Planning, University of North, Amol, Iran
LEAD_AUTHOR
-Fallah Monshadi, E and Ruhi, A. (2016) “An introduction to the requirements and strategies for achieving integrated urban transportation in Tehran”, Tehran City Studies and Planning Center, Tehran, 29-33.
1
-Gentile, G. and Meschini, L. (2011) “Using dynamic assignment models for real-time traffic forecast on large urban networks”, Proceedings second MTITS conference, Leuven, 22-24.
2
-Ghaderi,E, Kazemian, G, Bagheri, F. (2020) “The Assessment of Importance - Performance of Dimensions and the Indices of Integrated Management”, Geography and Sustainability of Environment, Tehran, 45-65.
3
-Kamrowska-Załuska, D. and Obracht-Prondzyńska, H. (2017) “Integrated Territorial Investments (ITI)” [in:] Medeiros, E., ed., Uncovering the Territorial Dimension of European Union Cohesion Policy. Cohesion, Development, Impact Assessment and Cooperation (Routledge Advances in European Politics), Routledge, New York, 14–127.
4
-Milakis, D. (2019) “Long-term implications of automated vehicles: An introduction” Transport Reviews, 39(1), 1–8.
5
DOI:10.1080/01441647.2019.1545286.
6
-Monshadi.F, E and Ruhim A. (2015) “Introduction to the requirements and strategies to achieve integrated urban transportation in Tehran”, Tehran City Studies and Planning Center, Tehran, 12-26.
7
-Ostadi.J.M and Rasafi,A. (2013) “Evaluation of sustainable development policies in the urban transport sector using dynamic system models; Case study: Mashhad”, Journal of Urban Management, Tehran, 281-294.
8
-Parsons Brinckerhoff (2012), “Integrated Transport and Traffic Management Plan and Bicycle Plan”, Consultation Document, 36-52.
9
-Pourhasan, A and Adelishahir, A. (2015) “Investigation of transportation challenges and damages in metropolises with a case study of Tabriz metropolis”, National Conference on the Use of New Technologies and Technologies of Design, Calculation and Execution in Civil Engineering, Architecture and Urban Planning. Tabriz, 1-12.
10
https://civilica.com/doc/465111
11
-Sajadi, M and Taghvaei, M. (2015) “Evaluation and analysis of sustainable urban transportation indicators”, Journal of Architecture and Sustainable City, Tehran, 3-7.
12
-Sausanis, J. (2011) “World’s Vehicle Population Tops 1 billion units”, Wardsauto, August 15, 2011,Available:http://wardsauto.com/ar/world_vehicle_population_110815%E2%80%99t-control growth- private-vehicle-official.html.
13
-Taghvaei, M, Zarabi, A and Salahi, H. (2019) “Analysis of the impact indicators on implementation of integrated urban management (Case Study:Tehran Metropolis)”, Urban Management Jurnal, Tehran, 263-265.
14
ORIGINAL_ARTICLE
Negative Emotions Recognition While Driving Using Electroencephalogram Signal
The role of the human factor has been confirmed as the number one cause of driving crashes and emotions are known as one of the most important factors of driver distraction. Although biological signals have a great potential for detecting emotions, so far few studies have been conducted to use these signals to develop emotion recognition systems while driving. Therefore, in this paper an electroencephalography (EEG) based classification model presents for recognizing low-valence high-arousal (LVHA) emotions (known as negative emotions) of drivers. For this purpose, two driving tests were designed in a driving simulator, one for driving under normal conditions and the other for driving in the negative emotional state. 18 people participated in these tests and the activity of four channels of their brain signals was recorded during the tests. The energies of delta, theta, alpha, beta, and gamma frequency bands, and the total signal energy along with gender were employed as inputs for classification models and emotional state was considered as output. Different models were used for subject-independent classification, among which the neural network classifier with an accuracy of 95% had the best performance. The results of the analysis showed that all channels are effective in increasing the accuracy of classifiers; also, gender has a relative impact on the accuracy of classification models. Assessing the effects of different frequency bands revealed that alpha and gamma bands have a greater effect on the accuracy of models than do other bands. At the end, different combinations of EEG channels were used to recognize negative emotions while driving, and the results indicated that using only two channels can help recognize these emotions with an accuracy of 89%.
http://www.ijte.ir/article_135996_49aa6f3aabe054237ada61ffbc4fca1b.pdf
2021-07-01
475
502
10.22119/ijte.2021.244943.1529
Emotion Recognition
negative emotions recognition while driving
Advanced Driver Assistant Systems (ADAS)
driving simulator study
promoting traffic safety
Naser
Habibifar
n.habibifar@email.kntu.ac.ir
1
Industrial Engineering School, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
Hamed
Salmanzadeh
h.salmanzadeh@kntu.ac.ir
2
Industrial Engineering school, K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
- Abou Elassad, Z. E., Mousannif, H., Al Moatassime, H. and Karkouch, A. (2020) “The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review”, Engineering Applications of Artificial Intelligence, Vol. 87, No., pp. 103312.
1
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ORIGINAL_ARTICLE
Optimizing Algorithm for Allocating Passengers in Shared Taxis
The issue of sharing vehicles has been riding since the '70s, but the advent of smartphones has made it a competitive choice to other transportation modes in recent years. The lack of restrictions on the movement of Internet-based passenger sharing systems leads to patrolling numerous personal vehicles in the network; this exacerbates congestion in high-traffic areas. On the other hand, the significant presence of circulating taxis and their non-optimal performance have disrupted the normal flow of traffic during peak hours and have led to an increase in travel time. This paper outlines a novel optimization algorithm for sharing repetitive and pre-planned trips. This algorithm is implemented on the midtown area network of Manhattan, New York, USA. Three scenarios were defined to simulate common services' status with the base scenario (do-nothing), which makes comparing possible with indicators such as distance travelled, and taxi occupancy ratio determined by passenger coefficient. Results of the first scenario - sending the nearest car - shows a decrease of 10.51%, the second scenario - allocating passengers to the nearest taxi - shows an increase of 10.16%, and finally the third scenario - the proposed algorithm - shows an increase of 25.56% in total mileage compared to the base scenario. Moreover, by defining Sharing Importance Factor (SIF) and using the proposed algorithm, it is possible to organize round-trip taxis, service repetitive and pre-planned trips, and significantly reduce the distance travelled throughout the network, and finally increase the passenger coefficient.
http://www.ijte.ir/article_135997_344c7c190e04721bd4f0568c6efef377.pdf
2021-07-01
503
520
10.22119/ijte.2021.279482.1564
Taxi Sharing
Allocation of Passengers
Optimization algorithm
Manhattan
Shariyar
Afandizadeh Zargari
zargari@iust.ac.ir
1
Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Samim
Shakoori
shakoori_6@yahoo.com
2
School of Civil Engineering, Iran University of Science and Technology
AUTHOR
Hamid
Mirzahossein
mirzahossein@eng.ikiu.ac.ir
3
Department of Civil -Transportation Planning and Engineering, Imam Khomeini International University
LEAD_AUTHOR
Mehrdad
Karimi
mehrdad_karimi_1390@yahoo.com
4
School of Civil Engineering, Iran University of Science and Technology
AUTHOR
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ORIGINAL_ARTICLE
A Social Engineering Optimizer Algorithm for a Closed-Loop Supply Chain System with Uncertain Demand
This paper presents a new model for a closed-loop supply chain problem under uncertainty. This model considers production, distribution, collection, recycling and disposal of items simultaneously. Because of the increased importance of the environmental factors, this model focuses on the revers flow of the supply chain and considers different types of technology for recycling centers. The model aims to minimize the establishment cost of centers, shipment cost, holding cost, collection cost and recycling cost. To face with uncertain parameters, a credibility-based possibilistic programming method is applied. Then, a social engineering optimizer algorithm is proposed to solve the problem efficiency. To validate the model and proposed algorithm, the results are compared with the results of GAMS. In addition, they prove the superiority of the proposed algorithm over a genetic algorithm to deal with problems and find better results in less running time. Finally, the behavior of the model is assessed by changing the values of parameters and the results are reported.
http://www.ijte.ir/article_89868_d380735e5dd02754dee01fbe878ea0f6.pdf
2021-07-01
521
536
10.22119/ijte.2019.168288.1456
Closed-loop supply chain
Uncertainty
social engineering optimizer
soroush
aghamohamadi
aghamohamadi.sor@ut.ac.ir
1
MSc. Student, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Masoud
Rabbani
mrabani@ut.ac.ir
2
Professor, School of Industrial Engineering, University f Tehran, Tehran, Iran
LEAD_AUTHOR
Reza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
3
UnProfessor, School of Industrial Engineering, University f Tehran, Tehran, Iran
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