Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Attached

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Moderate editing of English language required

Author Response

Reviewer 1

 

1. How was the new mode, MRT, integrated into the behavioral mode and mode/destination choice models in the demand simulator, and how does it differ from existing public transport services in Israel?

RE: Thank you for this question. The utility specification of the new mode, MRT, is similar to the utility specification of PT (which include buses only) In the absence of such a service, this is a conservative but reasonable assumption. The essential difference is due to walking, waiting and travel times resulting from the network modeling which are fed into the 7 mode and mode/destination choice models according to origin and destination choice. As there are 7 such models, which differ by trip purpose, with many various coefficients, we cannot describe all utility functions in the body of the paper, but additional information separating the components of the model can be attached (please see SI attached).

 

2. What factors led to the assumption that consumer preferences for MRT in Tel Aviv are similar to subway service in Boston?

RE: The wording in the text is not accurate. The full demand model was originally estimated for Boston and went a very thorough calibration process to adapt the behavior to that of Tel Aviv (see [21]). The correct explanation is this:

“As MRT is a new service in Israel, demand-based models consider that consumer choice in Tel-Aviv, which uses coefficients in its modeling, for MRT is similar to PT service in Tel-Aviv, along with MRT unique specifications of travel time and walking time to MRT stations. This is a conservative assumption in the absence of such a service and may lead to an underestimation of demand to MRT.”

This correction applied to the text – line 212.

3. What are the unique specifications of MRT, particularly concerning travel time and walking time to MRT stations, and how do they influence mode choices in the demand models?

RE: The unique specifications of MRT are derive from the network topology and modeling; they are considered in as part of the Tm  variable (please see SI attached)

 

4. How are mode shares classified into five mode classes (mass transit, active modes, on-demand, car/carpool, and other) in Figure 3, and what are the corresponding percentages for each class in the base year of 2017?

RE: Mode shares are classified as follows: mass transit (bus, light rail and metro), active modes)bicycle and walk(, on-demand (taxis(, car/carpool) car as driver, carpool with 2 passengers and 3 passengers) and other (motorcycle and private bus). This clarification was added to the text. The corresponding percentages for each class in the base year of 2017 is presented in Figure 3.

 

5. With the presence of the MRT system by 2040, what are the projected mode shares for mass transit, private car trips, active modes, on-demand, and other modes?

RE: With the presence of the MRT system by 2040, the projected mode shares are presented in Figure 3.

6. What are the reasons behind the significant increase (390%) in mass transit trips and the corresponding decrease in private car trips (22%) in the future year 2040?

RE: The significant increase in mass transit trips can be explained by the high accessibility level as most of the metropolis will be accessible by an average walking time of 16 minutes to metro stations; in addition to the high frequency of the service - every 3 minutes, and significantly shorter travel times compared to public transport (3 times shorter) in the reality of a congested metropolis. This explanation was added to the text (line 224).

 

7. How does the anticipated increase (41%) in active modes' usage affect the overall transportation landscape in 2040?

RE: The anticipated increase in active modes' usage is a result of several changes in land use, new modes introduction (MRT) and population growth which are expected to be realized in 2040, it is not possible to isolate the effect of the increase in active modes' usage, but it can be said that following the expected changes, trips are expected to be shorter and less prolonged compared to 2017. This explanation was added to the text in line 229.

 

8. Why is the on-demand usage not expected to change in the future year 2040 despite the introduction of the MRT system and other changes in the transportation system?

RE: this On-demand usage is expected to reduced from 0.4% in 2017 to 0.3% in the future year 2040 -  a  decrease of 25%. To begin with, the percentage is very small as the service in Tel Aviv is very expensive compared to other modes. Therefore, the expected changes in 2040 leads to a reduction in on-demand usage since other modes (such as MRT) become much more attractive in terms of price to the consumer. This explanation was added to the text in line 230.

 

9. What potential challenges or barriers could arise during the implementation of the MRT system, and how might they impact the projected outcomes?

RE: There are many expected challenges in implementing a new MRT service. For example, low service reliability as reflected in long waiting times, low service frequency, inconvenience at boarding stations and overcrowding, all of these are just some of the problems that may arise. However, we assumed that the service is characterized by high reliability - as planned and reflected in short waiting times and high frequency. We did not explicitly assume the rest but made a conservative assumption that the service is not perfect and this is reflected in the alternative constant we used, which is the same as the bus service. So, overall, we reflect a better service compared to the existing bus service, but not perfect, so it is definitely possible that there is a slight underestimation of demand here. We thank the reviewer for this question and think it is a fascinating topic for further research. A short explanation was added to line 212 and also to the conclusion section in line 614.

 

10. How does the projected mode shift in 2040 align with the broader urban planning goals for the Tel Aviv metropolis in terms of sustainability, environmental impact, and accessibility?

RE: Overall, the projected mode shift in 2040 is aligned with the broader urban planning goals for the Tel Aviv metropolis in terms of sustainability, environmental impact, and accessibility. Results indicate less reliance on private transportation and significantly more public transportation usege and more walking and cycling. This explanation was added to line 234. Also, from an analysis we did and have now added to the paper on page 11, under 60% electrification in 2040, following the Ministry of Energy forecasts for 2040, a decrease of 57% in primary energy consumption is observed (Figure 6). In this scenario, including the mass transit system, fuel-based (gasoline and diesel) consumption decreases by an additional 5%. Total GHG emissions follow a similar trend where emissions are significantly reduced by 58%. The presence of a mass transit system reduces emissions by an additional 5%.

 

11. How were the network and its attributes used as inputs to the mesoscopic model and the dynamic traffic assignment in the simulation framework?

RE: The Tel-Aviv metropolitan supply model was coded at the most detailed level of network for Ayalon Highways. This work included detailed geometrical representation of roads, intersections and traffic devices; detailed description of the intersections (traffic lights, give-ways, stops), transit priority, actuated control, public transport plans, and associated parameters. Later, it was expanded and validated by the authors in [21]. The network for 2040 was not changed as compared to 2017 except for the MRT network. A clarification was added to the text.

 

12. How were the O-D matrixes from SimMobility processed to fit Aimsun's format for use in the supply model of Tel-Aviv's transportation system?

RE: We used python scripts to select trips by modes (taxis and car trips separately from PT trips) from the activity diary generated by SimMobility. We then grouped them to half an hour OD matrices and send them to the Aimsun model using the correct format. Different scripts were used to select all the AMOD trips from the diary and generate a JASON file to be used as input for the Aimsun RIDE (Aimsun AMoD API) that add these trips to the model. A clarification was added to the text.

13. Can you elaborate on the process of calibrating and validating the dynamic traffic assignment for the 2017 conditions in Tel-Aviv's transportation system?

RE: A statistical comparison of response times in selected road sections, traffic counts, travel times and travel speeds compared to Google data were performed and compared, for morning peak, in order to determine whether the desired accuracy in reproducing the system behavior was achieved. The full calibration and validation results can be found in [21]. In addition, travel times were compared with destination origin pairs, for which travel time data from Google data were available (please see [21]).

 

14. How comprehensive is the MRT network model constructed for Tel-Aviv, and what are the sources used to determine all the lines and station locations?

RE: The MRT network is based on the Israeli DOT plan for mass transit in Tel-Aviv metropolitan area. It contains all the planed metro and light rail lines and station (now days some of them are in construction phases while others in different approval stages). The official .shp files can be found here: https://geo.mot.gov.il/. We assumed the lines’ frequency and speed based on well-known numbers form literature.

        A clarification was added to the text.

 

15. How accurate is the MRT network model in representing the actual conditions and constraints of the MRT system that will be implemented in Israel by 2040?

RE: The goal of this work was not to examine the planned MRT network but to examine a prototype city resembles Tel-Aviv metropolis for 2040. The lines and station’s locations are fully compatible with the planned lines as we know today. Of course, this plan is subject to future changes. We didn’t consider the capacity of the lines which may affect the waiting time and the level of service. Future work may consider the capacity constraints to the MRT lines as well as gradual implementation of the lines.  This point was added to the text on line 614.

 

16. What are the key differences between the simulated MRT network model and the plans of the Israeli Ministry of Transportation (MOT) for the MRT system implementation?

RE: The simulated MRT network is based on the Israeli MOT – we used the most recent plans which have been published.

 

17. How were the transport network supply model parameter specifications selected for the Tel-Aviv metropolitan area, and what considerations were taken into account during this process?

RE: As mentioned in our response to question 16, our goal was not to replicate exactly Tel-Aviv metropolis condition, but to use it as a prototype city. We made efforts to represent the road hierarchy, traffic control plans and other specifications as accurate as possible; for the rest, we used Aimsun default parameters (on which please see [21]).

18. Were any assumptions or simplifications made during the selection of transport network supply model parameters, and if so, how might they impact the accuracy and reliability of the simulation results?

RE: Some simplifications were made in order to enable the running of the mesoscopic model for such a large and crowded network. First, we didn’t include local roads in the network so that the centroids were connected directly to the collectors and primary roads. Thus, our road network does not encompass short trips (up to 1.5 km), which may further contribute to air pollution. Second, we replaced some of the roundabouts with un-signalized intersections to prevent unrealistic gridlocks. As a result of those simplifications, the model is less accurate in representing local condition in specific zones. It should be noted that, in general, mesoscopic models are less accurate in representing local conditions, microscopic models are more appropriate for that goal. In the future it is possible to improve those parts of the network and use a hybrid simulation, so that we could learn the effects of local conditions on the overall large network (e.g., prioritizing public transit on a specific corridor). These limitations were added to a new section as part of the Conclusion (please see line 614).

 

19. What methods were used to validate the results obtained from the simulation framework, especially considering the significant changes to the transportation system due to the MRT system implementation?

RE: It is not possible to perform validation for future planning. At the same time, each significant change was examined separately and compared to the baseline condition. For example, when we built the synthetic population for 2040, we simulated the results against the population of 2017, then added the MRT and tested the results for the base situation in 2040 without MRT. In addition, we compared our results, as much as possible, to the Tel Aviv model that was used to generate the forecasts by the planning authorities (Ayalon Highway).

 

20. How were the uncertainties associated with the model inputs and assumptions accounted for in the simulation framework?

RE: We perform sensitivity analysis to model inputs.  AMoD sensitivity to cost was tested in former study, as mentioned earlier, and can be found in [21].   Fuel prices elasticities of trip demand can be found in table 3.  Sensitivity to the level of electrification and emissions was also tested extensively and now it is added to page 11 as Figure 6.

 

21. To what extent does the simulation framework consider potential future changes or developments beyond the planned MRT system, such as changes in population density, land use, or technological advancements in transportation?

RE: Changes in population density, and land use are reflected in the simulation framework as a full synthetic population was built for 2040. Land use changes were considered as well following the information received by the planning authority (Ayalon Highways).  Technological advancements in transportation such as the introduction of AV’s in the form of on-demand service are the heart of our analysis but there may be many other technological changes that we have not evaluated, but can be evaluated as part of the simulation framework; these are the subject for further research.

 

22. What specific pollutants and emissions are considered in the energy consumption and emission model, and how are they calculated for each road section by the time of day?

RE: The pollutants to be evaluated as part of this study are carbon dioxide (CO2), nitrogen oxide (NOX), fine particulate matter (PM2.5), and volatile organic compounds (NMVOC). Scripts that calculate the amount of emissions for each road section in units of grams and grams per km were built based on the simulation outputs, such as road density, volume, and average speed. The correlation between the information coming out of the simulation and emission from each vehicle is made by the emission factor. The emission factor is a representative value that relates pollution to activity. The HBEFA )the Handbook Emission Factors for Road Transport) database connects the emission factor to the road jam and type as well as car characteristics such as European emission standards and engine size (see [30]). This explanation was added to the text on page 10.

 

23. How does the energy consumption and emission model account for changes in fuel consumption, CO2, GHG emissions, NOX, PM2.5, and NMVOC emissions resulting from different types of vehicles?

      RE: Please see our reply to question num. 23.

 

24. What are the implications of the projected dramatic changes in fleet composition by 2040, as forecasted by the Ministry of Energy and Infrastructure?

RE: The implications of the projected dramatic changes in fleet composition by 2040 are now added and discussed in line 333 and Figure 6.

 

25. How does the expected shift towards Battery Electric Vehicles (BEV) impact the overall energy consumption and emissions in the metropolitan area of Tel Aviv?

RE: Please see our reply to question X regarding the implications of the expected shift towards Battery Electric Vehicles (BEV) in terms of energy consumption and emissions.

 

26. How were the Ministry of Energy and Infrastructure fleet composition forecasts for 2040 integrated into the model to estimate the fleet composition mix of private vehicles?

RE: Our mesoscopic emission model assumes that the distribution of vehicle characteristics on a particular road section is the same as the characteristics of vehicles at the state level. Fleet composition for 2040 is based on the composition of the fleet at the household level in 2017 as a base, after which we randomly changed the composition of the fleet to match the 60% electrification assumption of the Ministry of Energy and Infrastructure. A clarification was added to the text on line 319.

Reviewer 2 Report

Comments and Suggestions for Authors

This research paper investigates the potential impacts of automated mobility-on-demand (AMoD) services on urban transportation in the Tel-Aviv metropolitan area for the year 2040. The paper uses simulation (SimMobility MT and Aimsun) explores various scenarios involving AMoD integration with transit and car ownership reduction. There are three major area the revision should work on:

Limited Scope: The study focuses on one metropolitan area, and while it makes comparisons with other prototype cities, the findings may not be universally applicable. Urban characteristics, policies, and behaviors can vary significantly between regions. The authors either need to draw (and prove) more general insights, or I prefer calling this a case study of Tel-Aviv (in the title, abstract, and intro).

Assumptions on Fleet Composition: The paper assumes a fully electrified AMoD fleet, which may not be realistic. The availability and adoption of electric vehicles can vary by region and over time. Also, the authors completely did not consider other strategies for sustainable urban mobility which will interact with the AmoD fleets. For example, the authors should review micromobility: Mangold, Max, et al. "Geo-fence planning for dockless bike-sharing systems: A GIS-based multi-criteria decision analysis framework." Urban Informatics 1.1 (2022): 17; Zhao, Pengxiang, et al. "Impact of data processing on deriving micro-mobility patterns from vehicle availability data." Transportation Research Part D: Transport and Environment 97 (2021): 102913.

 

Oversimplification of AMoD Introduction: The scenarios consider relatively simplistic introductions of AMoD, without addressing potential regulatory, market, or consumer behavior complexities that may arise during implementation. Also, while the paper mentions equity analyses in passing, it does not delve into the potential equity implications of different scenarios, which is a critical consideration in transportation planning. Overall, the paper briefly mentions policy interventions but does not provide a detailed discussion of the policy implications of its findings, which could be valuable for policymakers.

Comments on the Quality of English Language

N.A.

Author Response

 

Reviewer 2

 

This research paper investigates the potential impacts of automated mobility-on-demand (AMoD) services on urban transportation in the Tel-Aviv metropolitan area for the year 2040. The paper uses simulation (SimMobility MT and Aimsun) explores various scenarios involving AMoD integration with transit and car ownership reduction. There are three major area the revision should work on:

Limited Scope: The study focuses on one metropolitan area, and while it makes comparisons with other prototype cities, the findings may not be universally applicable. Urban characteristics, policies, and behaviors can vary significantly between regions. The authors either need to draw (and prove) more general insights, or I prefer calling this a case study of Tel-Aviv (in the title, abstract, and intro).

RE: Thank you for this suggestion. We agree that framing the study as a case study of Tel-Aviv metropolis would be more accurate. Thus, the title and all relevant parts were changed accordingly.

Assumptions on Fleet Composition: The paper assumes a fully electrified AMoD fleet, which may not be realistic. The availability and adoption of electric vehicles can vary by region and over time. Also, the authors completely did not consider other strategies for sustainable urban mobility which will interact with the AmoD fleets. For example, the authors should review micromobility: Mangold, Max, et al. "Geo-fence planning for dockless bike-sharing systems: A GIS-based multi-criteria decision analysis framework." Urban Informatics 1.1 (2022): 17; Zhao, Pengxiang, et al. "Impact of data processing on deriving micro-mobility patterns from vehicle availability data." Transportation Research Part D: Transport and Environment 97 (2021): 102913.

RE: We agree that the availability and adoption of electric vehicles can vary by region and over time. The Israeli Ministry of Energy and Infrastructure has set a strict goal that all public vehicles will be fully electric by 2040 and 60% of private vehicles will be fully electric. Given this goal it was natural to assume that the taxi fleet would be entirely electric, of course other drivetrains could be tested. In this paper we have chosen to focus on AMoD strategies and its interaction with various modes including bikes while other micro mobility strategies are definitely worth testing, this is the program for further research. A discussion regarding additional sustainable strategies such as those suggested by the reviewer has been added to the conclusion section on line 636 as well as the recommended references.

Oversimplification of AMoD Introduction: The scenarios consider relatively simplistic introductions of AMoD, without addressing potential regulatory, market, or consumer behavior complexities that may arise during implementation. Also, while the paper mentions equity analyses in passing, it does not delve into the potential equity implications of different scenarios, which is a critical consideration in transportation planning.

RE: What seems to be an oversimplification of AMoD introduction is actually based on our preliminary experiments (see [21]) where potential market and consumer behavior complexities are considered which reflected in various service costs. In the same experiment and also in [28] we also carried out a thorough analysis of potential equity implications of different AMoD scenarios. This, however, was not the focus of this study.

Overall, the paper briefly mentions policy interventions but does not provide a detailed discussion of the policy implications of its findings, which could be valuable for policymakers.

RE: Discussion section was improved and is entirely dedicated to the implications of the chosen strategies, also in a broad view and in comparison, to other cities, with a different topology, where the same strategies were tested.

Reviewer 3 Report

Comments and Suggestions for Authors

The paper entitled “Sustainable automated mobility-on-demand strategies in dense urban areas” is submitted for possible publication in the MDPI Sustainability journal. The paper sets out to quantify the effects of automated mobility on demand on trip patterns, congestion, energy, and emissions in a dense, transit-oriented prototype city. Overall, the paper contains useful information. However, major revisions are needed at this stage.

Comments

1.      I think the authors ought to present the users' perspective of AMoD – studies that addressed how users are willing to use such services and why.

2.      I think the novel element of this paper should be clearly stated. I feel the introduction section is short and lacks clarity.

3.      Authors should consider revising the title of the paper to “Sustainable automated mobility-on-demand strategies in dense urban areas: the case study of Tel Aviv 2040”. This is really a case study.

4.      The authors state “we synthesized the Tel Aviv metropolitan population for 2040 mainly based on the synthetic population of 2017 by randomly duplicating/reducing individuals along with their characteristics at each traffic zone.” What are the implications of such action. I have major concerns here. The term “randomly” has to be defined.

5.      I think reducing household private car ownership by 25% may not be a realistic strategy. Even with the introduction of a low-cost AMOD service. The authors have to come up to support their arguments.

6.      Following up with the above point (5) I think the authors should revisit all assumptions made in this simulation exercise.

 

7.      The authors out to state the limitations of the reported outcomes. Also the limitation of each simulation program used in this study. 

Comments on the Quality of English Language

Minor editing of the English language required

Author Response

Reviewer 3

 

Comments and Suggestions for Authors

The paper entitled “Sustainable automated mobility-on-demand strategies in dense urban areas” is submitted for possible publication in the MDPI Sustainability journal. The paper sets out to quantify the effects of automated mobility on demand on trip patterns, congestion, energy, and emissions in a dense, transit-oriented prototype city. Overall, the paper contains useful information. However, major revisions are needed at this stage.

Comments

1. I think the authors ought to present the users' perspective of AMoD – studies that addressed how users are willing to use such services and why.

RE: Studies that addressed the users' perspective of AMoD were added to the Introduction section as follows:

“ ….The acceptance of AMoD services by the users and their perception is also an emerging topic [14]. It was found that ease of access is an important factor for the acceptance of AMoD [15-20], as well as technological acceptance [15, 19], and the willingness to share the AMoD vehicle [21, 22]. A comprehensive review of recent and important AMoD studies can be found in [14].”

2. I think the novel element of this paper should be clearly stated. I feel the introduction section is short and lacks clarity.

RE: The introduction section was revised. The novel element of this paper is clearly stated as follows:

“This paper further contributes to the prototype-city simulation approach and thus makes significant contributions to the existing literature on AMoD impacts on future mobility in the following areas: (1) we generate a prototype city resembles large-scale dense, transit-oriented urban enviorment (Tel-Aviv metropolis/Gush Dan) using advanced agent-based simulation; (2) we developed and simulated AMoD scenarios in dense, transit-oriented cities. We consider (a) the naive introduction of AMoD, along with scenarios in which AMoD is (b) integrated with mass transit and (c) introduced along with a policy intervention to reduce household car ownership; and (3) we address demand–supply interactions and compare impacts to three other distinct urban typologies were similar policies were examined. Such comparison allow us to tie the results to the unic typology, so that our results are broadly applicable to cities in that typology and further demonstrate the viability of the prototype-city methodology.”

3. Authors should consider revising the title of the paper to “Sustainable automated mobility-on-demand strategies in dense urban areas: the case study of Tel Aviv 2040”. This is really a case study.

RE: Thank you for this comment. The title of this study was changed according to reviewer's suggestion.

4. The authors state “we synthesized the Tel Aviv metropolitan population for 2040 mainly based on the synthetic population of 2017 by randomly duplicating/reducing individuals along with their characteristics at each traffic zone.” What are the implications of such action. I have major concerns here. The term “randomly” has to be defined.

The randomness in the process is reflected in a random choice of the person whom we replicate in a certain TAZ. The number of people who were reproduced is contingent upon future predictions for the population in that TAZ for the year 2040. To validate the correctness of this process, we compared the resulting population against a more precise prediction - the number of individuals in each age group and gender within each TAZ to the predictions obtained by the regression, as shown in Figure 1. Although the only parameter for population replication is the general number of people in the TAZ, we were able to achieve very good result also in the age-gender resolution, thus we were  able to reflect very well the future population. A clarification as added to the text.

5. I think reducing household private car ownership by 25% may not be a realistic strategy. Even with the introduction of a low-cost AMOD service. The authors have to come up to support their arguments.

RE: Reducing car ownership has been put forward as a plausible and potentially beneficial policy approach for enhancing sustainability outcomes in urban environments [46, 47]. Singapore is perhaps the best documented; it has pursued a consistent policy for almost four decades in which car ownership and use have been controlled. As a result its level of car ownership is around a third of that of comparator cities [48]. Tel-Aviv metropolitan area is expected to grow and become similar to Singapore in many ways. This explanation was added to the text to support this scenario.

6. Following up with the above point (5) I think the authors should revisit all assumptions made in this simulation exercise.

RE: All assumptions made in this simulation exercise were carefully revisited.

7. The authors out to state the limitations of the reported outcomes. Also the limitation of each simulation program used in this study. 

RE: We thank the reviewer for this valuable suggestion. The limitations of this study and also plans for future studies were added to the conclusion section as follows:

“As with any simulation framework, there are limitations that arise from the system's design. In this study, freight mobility was not included both on demand and supply simulator, as the demand simulator is incapable of estimating freight demand within the existing framework. The presence of trucks may significantly influence air pollution and also impact traffic volume on roads by altering the flow on specific routes. Additionally, some simplifications were made in order to enable the running of the mesoscopic model for such a large and crowded network. First, we didn’t include local roads in the network so that the centroids were connected directly to the collectors and primary roads. Thus, our road network does not encompass short trips (up to 1.5 km), which may further contribute to air pollution. Second, we replaced some of the roundabouts with un-signalized intersections to prevent unrealistic gridlocks. As a result of those simplifications, the model is less accurate in representing local condition in specific zones. It should be noted that, in general, mesoscopic models are less accurate in representing local conditions, microscopic models are more appropriate for that goal. In the future it is possible to improve those parts of the network and use a hybrid simulation, so that we could learn the effects of local conditions on the overall large network (e.g., prioritizing public transit on a specific corridor).  Furthermore, in this study, the operator controlling the AMOD vehicle fleet is a very simple controller; it can’t model complex behaviors such as charging behavior, rebalancing, and mode changing within the trip which may affect both demand and supply. These are under development and are subjects for further research.  Finally, when introducing the MRT system, we didn’t consider the capacity of the lines which may affect the waiting time and the level of service. Future work may also consider the capacity constraints of the MRT lines as well as gradual implementation of the lines. Future studies may also consider other strategies for sustainable urban mobility such as micro-mobility [48], [49] and its interaction with the AmoD fleet, changes in population density, land use, or various technological advancements in transportation.”

Minor editing of the English language required

RE: English language editing was performed.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The literature review part can be enhanced by referring to recently published works on traffic/crowd management/parking-related papers. 

Ex: https://doi.org/10.1504/IJSNET.2021.119483  

Comments on the Quality of English Language

Minor changes and typos need to be taken care. 

Author Response

Reviewer 1

The literature review part can be enhanced by referring to recently published works on traffic/crowd management/parking-related papers. 

Ex: https://doi.org/10.1504/IJSNET.2021.119483  

RE: Thank you for this suggestion. The literature review was enhanced by referring to recently published works on traffic management and parking related papers as appears on page 2, line 47. The following references were added:

10. Nguyen-Phuoc, D. Q., Zhou, M., Chua, M. H., Alho, A. R., Oh, S., Seshadri, R., & Le, D. T. (2023). Examining the effects of Automated Mobility-on-Demand services on public transport systems using an agent-based simulation approach. Transportation Research Part A: Policy and Practice, 169, 103583.‏
11. Alotaibi, E. T. S., & Herrmann, J. M. Autonomous Mobility on Demand: From Case Studies to Standardized Evaluation. Frontiers in Future Transportation, 4, 1224322.‏
12. Choi, S., & Lee, J. (2023). An analytical model of parking infrastructure and fleet size optimization for Shared Autonomous Vehicles. Transportation Research Part E: Logistics and Transportation Review, 176, 103213.‏
13. Ghaffar, A., Shariat, N., & Hyland, M. (2022). Fleet Sizing for Robo-taxi Services: Comparing Novel and State-of-the-Art Scalable Modeling Approaches. Available at SSRN 4168352.‏
14. Beirigo, B. A., Negenborn, R. R., Alonso-Mora, J., & Schulte, F. (2022). A business class for autonomous mobility-on-demand: Modeling service quality contracts in dynamic ridesharing systems. Transportation Research Part C: Emerging Technologies, 136, 103520.‏
    
    
15. Wang, S., de Almeida Correia, G. H., & Lin, H. X. (2022). Modeling the competition between multiple Automated Mobility on-Demand operators: An agent-based approach. Physica A: Statistical Mechanics and its Applications, 605, 128033.‏       

Minor changes and typos need to be taken care. 

RE: English language editing was performed.

Reviewer 2 Report

Comments and Suggestions for Authors

I am happy with the revision which improved quality of the paper.

One minor comment: It's important to recognize that automated mobility-on-demand, while convenient, still contributes to energy consumption and traffic congestion. Meanwhile, I'm aware that micromobility is popular in Tel Aviv. Therefore, to establish a genuinely sustainable transportation system, it is imperative to integrate automated mobility-on-demand with micromobility options. e.g. see ideas in Schumann, H. H., Haitao, H., & Quddus, M. (2023). Passively generated big data for micro-mobility: State-of-the-art and future research directions. Transportation Research Part D: Transport and Environment, 121, 103795.

Comments on the Quality of English Language

N.A.

Author Response

Reviewer 2

I am happy with the revision which improved quality of the paper.

One minor comment: It's important to recognize that automated mobility-on-demand, while convenient, still contributes to energy consumption and traffic congestion. Meanwhile, I'm aware that micromobility is popular in Tel Aviv. Therefore, to establish a genuinely sustainable transportation system, it is imperative to integrate automated mobility-on-demand with micromobility options. e.g. see ideas in Schumann, H. H., Haitao, H., & Quddus, M. (2023). Passively generated big data for micro-mobility: State-of-the-art and future research directions. Transportation Research Part D: Transport and Environment, 121, 103795.

RE: Thank you for this important comment. The following explanation was added to Conclusion section, line 636:

“…It's important to recognize that automated mobility-on-demand, despite its benefits, still contributes to energy consumption and congestion as shown by this study. Thus, future studies may also consider other strategies for sustainable urban mobility such as micro-mobility [48-50] and its interaction with the AmoD fleet…”

Suggested reference was added (no. 50)

Reviewer 3 Report

Comments and Suggestions for Authors

The revised version is much better than the original submission. I have no further comments. 

Comments on the Quality of English Language

Minor editing of the English language required

Author Response

Reviewer 3

The revised version is much better than the original submission. I have no further comments.

RE: We thank the reviewer for the time and effort invested in this review.

Minor editing of the English language required

RE: English language editing was performed.