CET 97 DOI: 10.3303/CET2297007 Paper Received: 30 May 2022; Revised: 23 July 2022; Accepted: 7 August 2022 Please cite this article as: Kim D., Choi M., Ku D., Lee S., 2022, Economic and Environmental Impacts of Tag-less Framework, Chemical Engineering Transactions, 97, 37-42 DOI:10.3303/CET2297007 CHEMICAL ENGINEERING TRANSACTIONS VOL. 97, 2022 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Jeng Shiun Lim, Nor Alafiza Yunus, Jiří Jaromír Klemeš Copyright © 2022, AIDIC Servizi S.r.l. ISBN 978-88-95608-96-9; ISSN 2283-9216 Economic and Environmental Impacts of Tag-less Framework Dahye Kima, Minje Choia, Donggyun Kub, Seungjae Leeb,* aDepartment of Transportation Engineering/Department of Smart Cities, University of Seoul, Korea bDepartment of Transportation Engineering, University of Seoul, Korea sjlee@uos.ac.kr Mobility as a service (MaaS) has garnered significant academic attention recently. MaaS focuses on integrating various modes of transport so that mobility can be packaged as a single complete service. The main idea of MaaS is to combine different transport modes, such as public transport, bicycles, and PM, optimize trip efficiency and provide a complete process to passengers. There are many transport modes but each mode runs separately without a system that integrates them. The current public transport pricing system requires a card to be tagged when alighting and boarding, which causes congestions and delays in the operation of public transport. With a tag-less system, the connectivity among each mode can be improved by providing a comprehensive transport service, in which the time spent on tagging the cards can be reduced. This study aims to analyze the effects of a tag-less transportation system. First, a tag-less system can form a mobility platform, which increases the convenience of the transport system. This could be the foundation of MaaS and door-to-door services. Next, it can reduce the expenditure of transport operators, and adjust headways so that more people can use public transport. Finally, the travel time can be reduced, which can be a great reason to induce more people to use public transport instead of private cars. As the travel time decreases owing to adopting a tag-less system, the mode share of cars can be decreased approximately by 8.4 %. Furthermore, due to reduced mode share of car, environmental benefits are obtained. It is found that emissions can be reduced up to 15.8%. It is expected that a tag-less system can help realize the MaaS network with various other positive effects. 1. Introduction One of the most important issues currently addressed is reducing greenhouse gas (GHG) emissions. The revitalization of public transport has emerged as a key task in transportation as an effective method to reduce the use of passenger cars. Public transport can be an efficient mode choice in terms of energy and cost because many more people can be transported per unit vehicle compared with private cars (Jasim et al., 2021). One method to revitalize public transport is Mobility-as-a-Service (MaaS). Mulley summarized that MaaS is the core of transport services that provide customized mobility solutions according to individual needs. The core of MaaS is to integrate various modes of transportation into one digital platform so that arrival information, reservation, and payment can be made at once (ITS Australia, 2018). It is necessary to establish a mobile-based platform to integrate all the necessary processes for a trip. This study suggests a tag-less system to accomplish MaaS. In Korea, payment for public transport, taxis, or other transport modes is achieved by tagging system comprising a card and machine. Payments must be made for each transport mode individually. A tag-less system does not require a tag for payment because the payment is made through a single mobile app. In addition, it integrates all transport modes such that a user only needs to pay once per trip. Seoul has favorable conditions for implementing a tag-less system. The key to introducing the tag-less technology is the provision of integrated information on various modes. Because Seoul collects real-time information on most transportation, it has sufficient information to provide. In terms of infrastructure, Seoul is far more advanced compared to Helsinki, where the MaaS is most actively operating. This study aims to analyze the effects of a tagless system in Seoul. 37 2. Methodology 2.1 Effects of the tag-less system 2.1.1 Effect of the mobility platform Tag-less is a mobile-based public transport payment system that does not require card tagging. Because it is a mobile-based system, not only public transport but also PM, taxis, shared bicycles, or other transport mode can be integrated into one platform and provided to users. This is an important factor in realizing MaaS, which should be able to integrate customers, transport operators, data aggregators, and trusted MaaS advisors into one platform(Cruz and Sarmento, 2020). Tag-less allows each stakeholder to be grouped into one platform, which can facilitate the introduction of MaaS. Platforming mobility services also create added value. In the platform business, multiple participants, such as suppliers and users, share a joint platform. The value is created based on the interaction between participants, which leads to reduced transaction and operating costs per person as the number of participants increases. As the number of participants increases, the platform grows, and its attractiveness as an advertising platform improves; consequently, it can attract more companies. The platform business is a better way to adjust services. For example, as traffic behavior transformed in New York owing to the increase in telecommuting due to COVID- 19, Blade created and provided a new “Computer Pass” using helicopters, which created a new mobility field (Floetgen et al., 2021). Likewise, by integrating different modes under the tag-less system, it is possible to manage usage behavior simultaneously and provide appropriate services in response to changes in demand. 2.1.2 Increased convenience through integrated information provision Tag-less integrates each transport mode into one platform and delivers the entire information to the users at once. It combines various modes and proposes an optimal route to users to reach their destination. Users can select a route by comparing the travel cost and time of each mode, increasing the convenience. Bjorn analyzed the impact of information systems (IS) on travel in sustainable mobility. As a result, more than 80 % of survey respondents said they were willing to choose car sharing instead of private car if the convenience of the car sharing service increased owing to improved IS integration. The integration of information regarding various transport modes increases the convenience of use and, accordingly, can attract more users than before. 2.1.3 Reduced cost and headway Currently, a card must be tagged by the reader to pay for public transport. Transport operators spend a certain percentage of their income on fare adjustment and card fees, which is estimated to be approximately 35 billion won every year. The tag-less system can reduce costs and provide room for investment in improving public- transport operations. If the saved expenses are spent on additional subway cars, then the headway can be adjusted. In Seoul Metro Line 9, the number of subways cars have been supplemented from 4 to 6. As a result, headway and congestion were reduced by approximately 6 %. Zhou et al. (2020) found that the greater the headway is, the harder it becomes to secure sufficient dwell time for passengers during peak hours, which leads to a loss of capacity. If headway is adjusted with increased subway operations, the peak time capacity can be increased as well. Accordingly, the preferred mode of transportation can be converted from private cars to public transport. 2.1.4 Mode choice change If the payment for all transportation modes is made at once using the tag-less framework, travel time can be decreased. Zhao et al. (2021) found that at least 16 % of Maas adopters gave up car ownership when the MaaS platform was fully established. This study confirmed that MaaS has a mode-change effect. Feneri et al. (2020) analyzed the variations in users' choice of modes following the introduction of MaaS using the error component logit model. As a result, it was found that automobile users had a low tendency to continue using private cars, which proves the mode converting effect. Gkiotsalitis et al. (2022) stated that if a user purchases a ticket for all their travels at once through MaaS, they can deduce the origin and destination of door-to-door travel more accurately, which can reduce travel time. The study analyzed the effects of introducing MaaS on bus routes in Singapore and revealed that travel time was improved by 6 %. Jung (2018) studied the change in travel time and cost according to the adoption MaaS. As a result, he suggested that the subway travel time decreased by 2 % after implementation. Mode change can also cause environmental effects (Jang et al., 2021). This study analyzes the effect of the mode transition that can quantitatively confirm the positive effects of MaaS owing to the introduction of tag-less. 38 Figure 1. Framework of the study 2.2 Mode transition To compare the change in mode shares before and after tag-less, mode share ratios before and after the introduction of tag-less were calculated. The current mode share ratio was calculated based on the O/D distribution using the Korea Transport Database (KTDB). The KTDB constructs O/D and network data for Korea and Seoul metropolitan area for traffic demand estimation. The current mode share ratio was calculated based on 2019 data without considering the influence of COVID-19 (Ku et al., 2021). Because this study analyzes the change in mode share before and after the introduction of the tag-less system, an incremental logit model is used (Choi et al., 2021). The incremental logit model calculates the probability of selecting a mode based on the change in the mode utility measure (Mcfadden, 1974). The utility function presented by the Korea Development Institute was used in this study. The utility function and incremental logit model for each mode are shown as Eq(1) and Eq(2). 𝐶𝐶𝐶𝐶𝐶𝐶 = −0.0305128 × 𝛼𝛼 − 0.0142173 × 𝛽𝛽 + 2.15846 𝐵𝐵𝐵𝐵𝐵𝐵 = −0.0305128 × 𝛼𝛼 − 0.0305275 × 𝛽𝛽 + 0.892104 𝑆𝑆𝐵𝐵𝑆𝑆𝑆𝑆𝐶𝐶𝑆𝑆 = −0.0305128 × 𝛼𝛼 − 0.0305275 × 𝛽𝛽 + 2.34424 𝑇𝑇𝐶𝐶𝑇𝑇𝑇𝑇 = −0.0305128 × 𝛼𝛼 − 0.0142173 × 𝛽𝛽 − 2.08676 (1) where 𝛼𝛼 is travel time and 𝛽𝛽 is travel cost of each mode. 𝑃𝑃𝑖𝑖 ∗ = 𝑃𝑃𝑖𝑖 ∙ 𝑒𝑒𝑇𝑇𝑒𝑒 (∆𝑈𝑈𝑖𝑖) ∑ 𝑃𝑃𝑗𝑗 ∙ 𝑒𝑒𝑇𝑇𝑒𝑒 (∆𝑈𝑈𝑗𝑗) 𝐽𝐽 𝑗𝑗=1 (2) where 𝑃𝑃𝑖𝑖 ∗ is revised probability of choosing mode 𝑇𝑇, 𝑃𝑃𝑖𝑖 is current mode share of mode 𝑇𝑇, 𝑈𝑈𝑖𝑖 is utility function, and change ∆𝑈𝑈𝑗𝑗 is change of utility of mode 𝑗𝑗. The incremental logit model calculates a new mode share based on the change in utility of all modes. The utility function considers the travel time and cost for each mode. Because the travel time of buses and subways decrease when the tag-less framework is introduced, the travel time in the utility function is adjusted to calculate the mode share ratio after successfully launching the framework. The travel time change was set to 94 % for buses (Gkiotsalitis et al., 2022) and 98 % for subways (Jung, 2018). 2.3 Environmental benefits If a mode transition occurs with the tag-less framework, the traffic volume of passenger cars is expected to decrease, while that of public transport and shared mobility will increase. Ku et al. (2021) analyzed the effect of reducing traffic volume based on designating green transportation promotion areas and promoting eco-friendly transportation policies. By calculating the coefficient for each pollutant according to the changed traffic volume, it was found that the carbon dioxide reduction effect was 38.7 % before and after implementation (Kuet al., 2021). Accordingly, this study intends to calculate the benefits of reducing air pollution after successfully implementing the tag-less framework. First, the difference in vehicle kilometre travelled (VKT) before and after the introduction of the framework. The KTDB presents the emission coefficient for each pollutant by vehicle type, 39 which is used here. The annual pollutant reduction amount can be obtained by multiplying the difference in VKT before and after the tag-less implementation by the pollution coefficient. The pollution coefficients for each vehicle type are presented in Table 1. The value of pollutant savings (𝑃𝑃𝑆𝑆) is derived through Equation (3). 𝐷𝐷𝑙𝑙𝑙𝑙 and 𝐷𝐷′𝑙𝑙𝑙𝑙 are vehicle-km by link 𝑙𝑙 and by vehicle type 𝑘𝑘, respectively, without or with tag-less, and 𝐸𝐸𝑙𝑙𝑙𝑙 𝑣𝑣 is air pollution factor. 𝑃𝑃𝑆𝑆 = 𝑃𝑃𝑆𝑆𝑤𝑤𝑖𝑖𝑤𝑤ℎ𝑜𝑜𝑜𝑜𝑤𝑤 − 𝑃𝑃𝑆𝑆𝑤𝑤𝑖𝑖𝑤𝑤ℎ 𝑃𝑃𝑆𝑆𝑤𝑤𝑖𝑖𝑤𝑤ℎ𝑜𝑜𝑜𝑜𝑤𝑤 = ∑ ∑ ∑ 𝐷𝐷𝑙𝑙𝑙𝑙𝐸𝐸𝑙𝑙𝑙𝑙 𝑣𝑣𝐾𝐾 𝑙𝑙=1 𝐿𝐿 𝑙𝑙=1 𝑉𝑉 𝑣𝑣=1 𝑃𝑃𝑆𝑆𝑤𝑤𝑖𝑖𝑤𝑤ℎ = � � � 𝐷𝐷′𝑙𝑙𝑙𝑙𝐸𝐸𝑙𝑙𝑙𝑙 𝑣𝑣 𝐾𝐾 𝑙𝑙=1 𝐿𝐿 𝑙𝑙=1 𝑉𝑉 𝑣𝑣=1 (3) Table 1: Car air pollution factor (g/km) Speed CO NOx VOC PM2.5 CO₂ 50 0.38 0.22 0.03 0.01 145.34 3. Results 3.1 Cost reduction To analyze the benefits owing to reduced cost regarding transport operator charges, the number of additional trains that can be scheduled using the accumulated savings. Currently, Seoul has 10 metro lines with 325 stations, covering a total distance of 343.4 km, similar to the subways in cities such as London, New York, and Beijing. Accordingly, approximately 20 million big data points are generated per day. The total income of the operating institution is around 7 trillion KRW/y. The transportation business income of subway operators is around 2.3 trillion KRW/y, and the commission rate for transportation cards is 1.5 %, resulting in approximately 35 billion KRW/y in annual commission expenditure. According to the Seoul Metropolitan Government's Transportation Policy Division, the cost of adding one train is approximately 9.1 billion KWR. Approximately four additional trains can be arranged. Line 9, which is congested the most during peak hours, has a headway of approximately 8 min. If four more trains are arranged, the headway can be reduced to 6.3 min. Accordingly, it is possible to transport more passengers and induce a mode transition. The vehicle cost is 𝛾𝛾 and operational cost and depreciation cost of the vehicle is 𝛿𝛿 and 𝜀𝜀. 𝑁𝑁𝐵𝐵𝑁𝑁𝑆𝑆𝑒𝑒𝐶𝐶 𝑜𝑜𝑜𝑜 𝐴𝐴𝐴𝐴𝐴𝐴𝑇𝑇𝐴𝐴𝑇𝑇o𝑛𝑛𝐶𝐶𝑙𝑙 𝑉𝑉𝑒𝑒ℎ𝑇𝑇𝑖𝑖𝑙𝑙e = Revenue × 1.5 % (𝛾𝛾 + 𝛿𝛿 + 𝜖𝜖) × 6 (4) 𝑁𝑁𝑒𝑒𝑆𝑆 ℎ𝑒𝑒𝐶𝐶𝐴𝐴𝑆𝑆𝐶𝐶𝑆𝑆 = 𝑃𝑃𝑒𝑒𝐶𝐶𝑘𝑘 𝐻𝐻𝑜𝑜𝐵𝐵𝐶𝐶 𝑁𝑁𝐵𝐵𝑁𝑁𝑆𝑆𝑒𝑒𝐶𝐶 𝑜𝑜𝑜𝑜 𝐴𝐴𝐴𝐴𝐴𝐴𝑇𝑇𝐴𝐴𝑇𝑇𝑜𝑜𝑛𝑛𝐶𝐶𝑙𝑙 𝑉𝑉𝑒𝑒ℎ𝑇𝑇𝑖𝑖𝑙𝑙𝑒𝑒 (5) Table 2: Reduced cost and headway Revenue(KRW) Fee(KRW) Cost for a train(KRW) Additional vehicle Headway(min) Before After 2,305,507,611,259 34,582,614,169 9,102,000,000 4 8.0 6.3 3.2 Modal shift First, the current mode share ratio was calculated. The data used were taken from the Seoul metropolitan area network and O/D database built and distributed by the KTDB in 2019. The metropolitan area includes Seoul, Gyeonggi, and Incheon, and includes road and subway networks. The O/D in the metropolitan area is provided with respect to the modes; in this analysis, the mode share ratio of non-execution is calculated based on the sum of cars, buses, subways, and taxi O/D values. The mode share rate after introducing the tag-less framework was calculated using an incremental logit model. The utility for each mode comprises the travel time and cost. Because the travel time decreases after implementation, the changed mode share is calculated by adjusting the corresponding value. The travel times for buses and subways were applied at the previously set 94 % and 98 % levels. As a result of the modal split, the mode share rate of passenger cars decreased by 8.4 %, while that of buses and subways increased by 1.0 % and 8.1 %. 40 Table 3: Mode share change Mode Traffic volume (Trips) Mode share (%) Before After Before After Car Bus 21,198,900 17,842,237 52.8 44.5 (-8.4) 6,995,110 7,396,375 17.4 18.4 (+1.0) Subway 8,748,980 11,998,992 21.8 29.9 (+8.1) Taxi 3,169,900 2,875,285 7.9 7.2 (-0.7) Figure 2: (a)Mode share change, (b)Decreased traffic volume after adopting the Tag-less system 3.3 Environmental benefits From the mode transition analysis, it was shown that the traffic volume of passenger cars decreased, while that of public transport increased after tag-less implementation. Having fewer private cars in traffic results in environmental benefits; the pollutant reduction rate was calculated accordingly. Firstly, the VKT at the time of non-execution was 3,355,809,804 veh-km. The VKT at the time of implementation was 2,824,446,314 veh-km. The coefficient for each pollutant was multiplied by VKT to calculate the difference between the pollutant emissions before and after implementing the tag-less framework. Because Seoul implements a 50 km/h speed limit, the annual pollutant reduction was calculated considering 50 km/h. Consequently, after the introduction of the tag-less framework, carbon dioxide emissions decreased by 28,188,355 t (15.8 %). Table 2: Air pollutants reduction (Unit: t) CO NOx VOC PM2.5 CO₂ Before 465,451 269,472 36,746 12,249 178,022,690 After 391,751 226,803 30,928 10,309 149,834,335 Difference 73,700 42,668 5,818 1,939 28,188,355 4. Conclusion In Seoul, public transport payments are made using a transportation card system. The currently used transportation card system allows transfers between public transportation modes; the modes are not integrated. By introducing the so-called tag-less framework, it is possible to establish a system where information provision and payments are made at once by integrating each transport mode into one mobile platform. If different transportation modes are integrated in this manner, effects such as the convenience of information provision and time reduction can be expected. In this study, the mode transition effect that reduces travel time, which can be quantitatively measured, was analyzed. The analysis confirmed that the traffic volume generated by passenger cars decreased while that generated by buses and subways increased. Pollutant emissions decreased as private car volume decreased as well. The annual reduction in emissions for each pollutant type was calculated. Subsequently, it was determined that 28,188,355 t of carbon dioxide emissions could be reduced in the metropolitan area. There are other effects than mode transition. As a new mobility platform is created, convenience increases and costs decrease; it is possible to secure more users. Subsequently, more added value can be created with advertising revenue. Public transport based on a tag-less system would be a facility used by many citizens, it would be possible to create a new field of added value creation through advertisements placed at shops inside (a) (b) 41 the subway and partnerships with other businesses in the private sector. To integrate transportation methods operated by different operators into one single platform, government-level support to secure data sharing and compatibility is essential. AHP analysis can be used to consider these various factors. AHP analysis is a technique for measuring the relative importance by stratifying evaluation items that is suitable for a business with many factors, such as the tag-less framework. To successfully introduce this framework, further research to discover policies, costs, and benefits is necessary. Acknowledgments This work was supported by the Ministry of Education of the Republic of Korea, the National Research Foundation of Korea, and financially supported by the Korea Ministry of Land, Infrastructure, and Transport (MOLIT) as an ˹Innovative Talent Education Program for Smart City˼. References Buchanan P., Thomas D., Ryan S., 2018, Mobility as a Service in Australia Steering Committee Editorial Contributor, Port Melbourne, Australia. Choi M., Ku D., Lee S., Lee S., 2021, Environmental Impact of Personal Mobility in Road Managements, Chemical Engineering Transactions, 89, 331–336. Cruz C.O., Sarmento J.M., 2020, “Mobility as a service” platforms: A critical path towards increasing the sustainability of transportation systems, Sustainability, 12(16), 6368. Feneri A.M., Rasouli S., Timmermans, H.J.P., 2020, Modeling the effect of Mobility-as-a-Service on mode choice decisions, Transportation Letters, 1–8. Floetgen R.J., Strauss J., Weking J., Hein A., Urmetzer F., Böhm M., Krcmar H., 2021, Introducing platform ecosystem resilience: leveraging mobility platforms and their ecosystems for the new normal during COVID- 19, European Journal of Information Systems, 30(3), 304–321. Gkiotsalitis K., 2022, Coordinating feeder and collector public transit lines for efficient MaaS services, EURO Journal on Transportation and Logistics, 11, 100057 Hildebrandt B., Hanelt A., Piccinini E., Kolbe L., Nierobisch T., 2015, The Value of IS in Business Model Innovation for Sustainable Mobility Services-The Case of Carsharing, Wirtschaftsinformatik Proceedings 2015, 68, Osnabrück, Germany. Jang, Y., Ku, D., Lee, S. 2021, Environmental Benefit Calculation on Personal Mobility, Chemical Engineering Transactions, 89, 289–294. Jasim I.A., Farhan S.L., Hasan H.M., 2021, Ways to Activate Urban Transport to Achieve Urban Sustainability, IOP Conference Series: Materials Science and Engineering, 1090(1), 012034. Jung D., 2018, An evaluation of Maas service effect based on real travel data, American Journal of Engineering Research, 7, 37–43. Ku D., Kwak J., Na S., Lee S., Lee S., 2021, Impact assessment on Cycle Super Highway Schemes, Chemical Engineering Transactions, 83, 181–186. Ku D., Kim J., Yu Y., Kim S., Lee S., Lee S., 2021, Assessment of Eco-Friendly Effects on Green Transportation Demand Management, Chemical Engineering Transactions, 89, 121–126. Mcfadden D., 1974, The measurement of urban travel demand, Journal of Public Economics, 3, 303-328. Mulley C., 2017, Mobility as a Services (MaaS)–does it have critical mass, Transport Reviews, 37(3), 247–251. Zhao X., Andruetto C., Vaddadi B., Pernestål A., 2021, Potential Values of Maas Impacts in Future Scenarios, Journal of Urban Mobility, 1, 100005. Zhou Y., Bai Y., Guo H., Li T., Qiu Y., Zhang Z., 2020, Metro Scheduling to Minimize Travel Time and Operating Cost Considering Spatial and Temporal Constraints on Passenger Boarding, IEEE Access, 8, 114190– 114210. 42 007.pdf Economic and Environmental Impacts of Tag-less Framework