ABSTRACT. Problem statement The significance of understanding users’ behavior is well-known in transportation, especially when route choice modeling is the objective. Many studies have been conducted over the years, investigating the problem in a variety of aspects, focusing mainly on car drivers’ behavior (Prato, 2009). However, understanding the preferences of public transport users is essential for public transport network and service planning. A particular problem related to route choice models is the generation of the alternative choice set, which involves the recognition of all possible routes and chosen alternative. To estimate route choice models, it is necessary to know not only the entire choice set but also its attributes (Manohar at el, 2023). Revealed Preferences (RP) surveys, such as the Tel-Aviv household travel survey, provide detailed information on the individuals, which allows for a better understanding of their preferences. However, typically RP data include only the chosen alternative, and the modeler needs to infer the choice set in order to estimate the model.

Research objective The main objective of this paper is to analyze public transport route choice behavior. The paper will compare the chosen alternative for public transportation with possible route alternatives that will be generated using different pathfinding methods.

Methodological approach The suggested methodology employs a data-driven approach for generating the alternatives for the choice set. This methodology relies on several steps: • Dataset source: Tel-Aviv Metropolitan household travel survey conducted in 2016-2017. • Selection of observations related to bus travelers. • GPS survey raw data collection and processing. • Map matching for route identification. • Public transport choice set generation using different pathfinding methods. • Coverage of the different methods with respect to the map-matched route (Bekhor, Ben-Akiva, Ramming, 2006).

Expected results, conclusions, and possible implications This paper will report two major comparisons. First, the coverage of the different generation methods will be analyzed. Next, the differences between the observed RP route and the generated alternatives will be explained using network and personal variables available in the dataset. The paper will Illustrate how conventional methods are able to cover the real chosen alternative and aims to be a foundation for choice set generation for public transport modes. The generated choice set will form the basis for estimating public transport route choice models, which will be conducted in future research.

Bekhor, S., Ben-Akiva, M.E. & Ramming, M.S. Evaluation of choice set generation algorithms for route choice models. Ann Oper Res 144, 235–247 (2006). https://doi.org/10.1007/s10479-006-0009-8

Prato, C.G., 2009. Route choice modeling: past, present and future research directions. Journal of Choice Modelling 2 (1), 65–100. https://doi.org/10.1016/S1755- 5345(13)70005-8.

Mepparambath M.R, Sheng Soh Y, Jayaraman V, En Tan H, Azfar R.M, 2023. A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data. Transportation Research Part A 170 (2023) 103615. https://doi.org/10.1016/j.tra.2023.103615

ABSTRACT. Multiple studies show that ethnic minorities exhibit travel behavior patterns that differ from majority populations. This study explored the special case of Israel, which has a majority Jewish population and a minority Arab population. While many minorities population tend to be (descendants of) immigrants which may shape their travel behavior, the situation in Israel is different: the vast majority of both Jews and Arabs currently living in Israel has been born and raised there. At the same time, the two population groups tend to live in distinct towns and cities, with only a minority of Arab households living in Jewish towns and virtually no Jew living in an Arab town. Given this rather unique situation, we compare the travel behavior of three distinct groups: Jews living in (predominantly) Jewish towns, Arabs living in Arab towns, and Arabs living in (predominantly) Jewish towns. We ask whether the travel behavior of Arabs who moved to Jewish town mostly resembles the behavior of other Arabs or of their Jewish counterparts. The study relies on a large-scale GPS based travel behavior survey carried out in the Tel Aviv metropolitan area by a government-owned company on behalf of the Israeli Ministry of Transport. The sample consisted of 13,506 households, of which 13,101 are Jewish households and 402 Arab households. From the latter group, 198 Arab households are living in Jewish settlements (‘Arab-movers’). Results showed that the travel behavior of ‘Arab-movers’ shows a stronger comparison with Jews than with ‘Arab-stayers’. Differences between the two Arab groups were particularly striking in terms of trip distance, average number of passengers and travel companions. It was also found that Arabs from Arab communities travel more than Jews living in Jewish settlements, with Arabs living in Jewish settlements traveling the least. In terms of trip distribution across the day, Jews and Arabs living in Jewish communities have similar travel patterns, as both groups travel mostly during the day (~62%) and less in the start or the end of it (~18%). In comparison, Arabs living in Arab communities travel less during the day (50%) and more at the start or at the end of the day (22%). Further exploration of this relation was examined in relation to gender, finding that the travel patterns across the day are similar for women and men among each of the three groups. The similarities between Arab-movers and Jews remained strong after controlling for income, education, households' size, age, gender, employment status, and car ownership level. Taken together, the results suggests that Arabs living in Jewish settlements adopt the travel behavior of their local peers. This may be the result of assimilation in the majority population, but it may also be a result of self-selection, with Arabs who are less rooted in the local community and tradition leave Arab towns to live in Jewish towns with less local peer pressure. More research is needed to confirm this hypothesis.

ABSTRACT. Encouraging people to adopt sustainable travel behaviors is a primary policy goal in the global effort to curb climate change and make cities more livable. Transportation scholars and practitioners have long been using a variety of measures to shape how people travel, from the modes they use and the locations they visit to the routes they take and their departure time. ‘Hard’ measures, like turning car lanes into dedicated bus lanes, target (infra)structural changes that aim to make public transport and active travel more efficient and attractive. Advancements in (mobile) information and communication technologies (ICTs) and their proliferation have increased the attractiveness of ‘soft’ measures. Such measures, like providing real-time information on public transport routes and schedules, including via dedicated mobile applications, target psychological and behavioral factors to motivate individuals to change the way they travel.

In the past decade and a half gamification, defined as the use of game design elements in non-game contexts (Bui et al., 2015; Deterding et al., 2011), has emerged as a potentially promising “soft” measure aimed at encouraging behavioral change. Gamification, has been applied to various domains, including to improve health, learning, and sustainability outcomes (Douglas and Brauer, 2021). In recent years, gamification has also been introduced to transportation contexts to encourage a shift towards sustainable travel behavior, including active travel and public transport use alongside reducing private vehicle use and greenhouse gas emissions (Yen et al., 2019). However, gamification transportation interventions have been applied mostly ad-hoc and currently lack a comprehensive theoretical framework to build upon.

This paper provides a step towards bridging this gap by systematically reviewing the research on transportation gamification interventions. The primary objective of the review was to understand the various elements and narratives that transportation gamification interventions employ and the underlying motivations they aim to tap. The review identified thirty-seven transportation gamification interventions from around the world that focus on a range of transport contexts. The identified interventions come from papers that were published in peer-reviewed journals, conference proceedings, and professional reports. The studied transportation gamification interventions were then analyzed using two theoretical frameworks: goal-framing theory (a psychology-behavioral motivation framework) and the Hexad framework (an established gamification player experience framework).

Goal-framing theory was used to identify the normative, hedonic, and gain motivations that transport gamification framings and game experiences aim to tap. The analysis shows that transportation gamification interventions target different goal frames in their framing of the intervention and the game experiences they apply. The reviewed studies tend to frame their gamification interventions around the Health-Gain and Environmental-Normative subgoals, each accounting for thirty-two percent of total game goal frames employed. The use of these goal frames can be expected as the majority of reviewed studies is focused on sustainable or active travel. In contrast, the within-game experience goal frames tend to target the hedonic and monetary subgoals. Specifically, Competition and Challenge are the most common experience subgoals, making up twenty-four and twenty-one percent of the experience subgoals used, respectively. This again reflects the focus of transportation gamification interventions on active travel campaigns. Interventions for almost all transport contexts target these subgoals through leaderboards that encourage competition and ad-hoc challenges.

Applying the Hexad framework provides further nuance by analyzing transport interventions in the context of the gamification literature. The Hexad framework distinguishes between six non-mutually exclusive player experiences—Players, Achievers, Philanthropists, Socializers, Free Spirits, and Disruptors—and links them to the motivations for participation and the game elements that relate to them. The analysis shows that the distribution of game elements that transportation interventions employ deviates from the Hexad framework through stronger focus on specific intrinsic (i.e., Achievers) and extrinsic (i.e., Players) motivations. Focusing on the motivations that the game elements aim to tap further shows that they concentrate on competence, competition, and challenge that drive the Players and Achievers player types. The remaining player experiences and play motivations are left largely untapped in the studied transportation gamification interventions.

Synthesizing the insights from the analyses on goal framing and the Hexad player experiences suggests there are motivations that current transportation gamification interventions do not tap. Targeting these motivations would expand the range of individuals that might be driven to make long-term travel behavior change. Gamification is not a one-size-fits-all solution; different users may respond differently to different game elements depending on their personality traits, goals, and context. Future transportation gamification interventions should incorporate game framings and elements that target a range of motivations to foster long-term behavior change among a wide range of individuals.

References Bui, A., Veit, D., Webster, J., 2015. Gamification – A Novel Phenomenon or a New Wrapping for Existing Concepts? 21. Deterding, S., Dixon, D., Khaled, R., Nacke, L., 2011. From game design elements to gamefulness: defining “gamification,” in: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments. Presented at the 15th international academic MindTrek conference: Envisioning future media environments, Tampere, Finland, pp. 9–15. Douglas, B.D., Brauer, M., 2021. Gamification to prevent climate change: a review of games and apps for sustainability. Current Opinion in Psychology, Psychology of Climate Change (2021) 42, 89–94. https://doi.org/10.1016/j.copsyc.2021.04.008 Yen, B.T.H., Mulley, C., Burke, M., 2019. Gamification in transport interventions: Another way to improve travel behavioural change. Cities 85, 140–149. https://doi.org/10.1016/j.cities.2018.09.002

ABSTRACT. This common study of the TAU Geosimulation Lab, Tel Aviv Municipality, and Motus LTD aims at analyzing shared e-scooters usage as a background for establishing a future municipal management policy. This paper presents the initial results of the analysis of 1M trips performed in March 2023 and the population and socio-economic data of Tel Aviv residents. Our preliminary conclusion is that scooters that park at locations where they are easy to find, and ride are used essentially more intensively than those parked elsewhere.

ABSTRACT. Passenger satisfaction is a key factor in usage encouragement and one of the important milestones of public transportation (PT) planning. In a study conducted on San Francisco in 2020 we examined Twitter posts about passengers' experiences using PT in the city. The study aimed to analyze textual data from Twitter to determine whether passengers' satisfaction with PT can be inferred without using surveys. We chose San Francisco as it had a more concentrated PT network that is easier to control. In addition, Twitter users are more active in the United States, and they express themselves concisely which allows the application of language processing tools. Mendez et al. (2019) further concluded that social network data provides a wide representation of respondents' representations and spatial distributions compared to traditional surveys. Data was downloaded from Twitter between March and June 2020, as the first confirmed COVID19 case in the city occurred in February 2020. Like other cities in the world, additional measures were taken in March to contain the spread of the virus in the city. The research data were downloaded in March when the city's policy of closing and social distancing already began. Corona restrictions in San Francisco were eased in June 2020. As part of this study, two linguistic analysis methods were applied, the first one being TextBlob (Loria 2018) for sentiment analysis. This method calculates the sentiment of each post based on the weight of emoji words in each. The more negative the score, the more negative the sentiment. Classification of grades into negative, neutral, and positive was then applied according to the grade range, so that grades between 1 and 0.6 are negative, between 0.6 and 0.8 are neutral, and the rest are positive. The second method is LDA Topic Analysis, which is divided into four categories. Based on the LDAvis system validation examination, we found that the data is best represented by dividing it into four topics: Service operation, Reliability, Physical facilities, Service quality. According to the findings obtained from the sentiment analysis, a downward trend was found in the number of tweets that were classified as negative, and an increase in those classified as positive between the months of March and June. Interestingly, this result is in line with Gkiotsalitis and Cats (2021) findings. according to their study they reported a decrease in road traffic and PT use as a result of lockdowns and social distancing measures. Additionally, they found that several studies noted improved PT performance and reduced delays during this period, likely due to the reduction in road traffic.

References: * Gkiotsalitis, K., & Cats, O. (2021). Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions. Transport Reviews, 41(3), 374-392. * Loria, S. (2018). textblob Documentation. Release 0.15, 2(8). * Méndez, J. T., Lobel, H., Parra, D., & Herrera, J. C. (2019). Using Twitter to infer user satisfaction with public transport: the case of Santiago, Chile. IEEE Access, 7, 60255-60263.

ABSTRACT. Public transport use requires travelers to comprehensively apply various competencies that can be categorized under the concept of public transport literacy (PTL). These include knowledge of reading and writing, spatial and digital reasoning, and even financial understanding. These challenges pose difficulties to anyone, especially older people, who depend more on public transport for everyday mobility. We designed a lab-based experiment with younger and older healthy adults. The experiment included operating the Moovit public transport app for regular and irregular trips. A between-group comparison was made of the accuracy of the retrieved information and duration of the operation by age and gender. The conclusion is that app design must be better suited to the users' competencies, especially older travelers who require more training and technical support.

ABSTRACT. Public transportation (PT) is a service provided by public or private agencies, which is available to all persons who pay the prescribed fare. Previous studies showed that public transportation is more environmentally sustainable, efficient, and economical than any other form of travel (Beirão & Cabral, 2007). Walking has important health benefits, a recent metanalysis that included data on almost 50,000 persons found that taking more steps per day was associated with a progressively lower risk of all-cause mortality, up to a level that varied by age (Paluch et al., 2022). PT, aside from being more sustainable than private cars, has a positive impact on health and wellbeing. According to Badland et al. (2017) those living in more walkable and PT-oriented neighborhoods are more likely to walk for transport and less likely to be overweight or obese. This analysis aims to develop and implement an algorithm for better integrating walking into commonly used trip planning apps and evaluating its effect at increasing walking. The proposed approach for walkability potential analysis involves using an open-source trip planner - OTP (OpenTripPlanner, 2020) to generate trip plans (itineraries) for a set of origin and destination (OD) pairs. For each OD pair, several itineraries will be generated, including all trip legs, the start and finish stops, mode, transit/walk times, waiting times and distances. The generated itineraries can be controlled by several parameters, such as modes (bus, car, walk, etc.), maximum transfers, maximal walking distance threshold, etc.. This can provide insights into the potential benefits of "more walking" in terms of travel efficiency and PT integration. Such benefits resulting from the longer walking distance could be: 1) additional trip options, 2) the potential of quicker and more reliable trips (less transfers and waiting time), and 3) improved wellbeing. Table 1 illustrates the above-mentioned advantages. OTP constructs (itineraries) for the same origin and destination with variable walking distance thresholds. Table 1 presents the main attributes (total trip duration, walking distance, and number of transfers) for 1000-, 1500-, and 2000-meters walking distance thresholds, along with the time split between transit (riding), walking, and waiting. Table 1: 4 Itineraries with different walking distance thresholds Trip option Walking distance threshold (m) Total trip duration (mins) Walking time (mins) Transit time (mins) Waiting time (mins) Walking distance (m) Number of transfers 1 1000 95.1 13.67 65.97 15.47 1030 2 2 1500 88.08 20.55 51.42 16.12 1575 2 3 1500 85.08 23.5 55.5 6.08 1779 1 4 2000 54.77 26.68 24.72 3.37 2005 1

As the walking distance threshold increases, the total duration of the trip decreases. Specifically, by avoiding one extra transfer, the passenger eliminates waiting time at the bus station. Therefore, passengers who are willing to walk more can potentially save time and enjoy a more direct and efficient trip.

References Badland, H. M., Rachele, J. N., Roberts, R., & Giles-Corti, B. (2017). Creating and applying public transport indicators to test pathways of behaviours and health through an urban transport framework. Journal of Transport & Health, 4, 208-215. https://doi.org/https://doi.org/10.1016/j.jth.2017.01.007 Beirão, G., & Cabral, J. A. S. (2007). Understanding attitudes towards public transport and private car: A qualitative study. Transport Policy, 14(6), 478-489. OpenTripPlanner. (2020). Multimodal Trip Planning. http://www.opentripplanner.org/ Paluch, A. E., Bajpai, S., Bassett, D. R., Carnethon, M. R., Ekelund, U., Evenson, K. R., Galuska, D. A., Jefferis, B. J., Kraus, W. E., Lee, I. M., Matthews, C. E., Omura, J. D., Patel, A. V., Pieper, C. F., Rees-Punia, E., Dallmeier, D., Klenk, J., Whincup, P. H., Dooley, E. E., . . . Fulton, J. E. (2022). Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. The Lancet Public Health, 7(3), e219-e228. https://doi.org/10.1016/S2468-2667(21)00302-9

ABSTRACT. Transit service reliability is important for increasing passenger satisfaction and maintaining transit ridership, contributing to environmental and societal benefits. Transport authorities are increasingly concerned with monitoring transit operations to detect, locate, predict, and prevent service disruptions. Big data are available from regular transport operations, but data are underused for service disruption detection. Service complaints form a powerful indicator of locating service disruptions that were experienced by passengers, and they are becoming more abundant with digital advancements. The Israeli National Public Transport Authority (NPTA) maintains a civic complaint system with 91,368 annual complaints, of which 32,000 are about reliability issues. However, so far these complaints were checked separately only for enforcement purposes. Recent studies argue that smart handling of complaints can be used for better management of smart cities, and have shown that using data science for system-wide complaint analysis can generate important insights for improving public transport.

This study suggests a new approach to using passenger complaints to improve the automatic detection and prevention of service disruptions. Specifically, we suggest combining public complaints with big data related to transit operations, to identify structural weaknesses in the system and amend them. The study uses the NPTA's data set including 26 million annual rides on 10,200 buses along 7181 bus lines (rural, urban, and inter-urban) operated by 34 agencies. The data is compiled from several resources including GPS vehicle tracking, bus fleet data, ticket validations, and the GTFS line operations data. The two main data sources, ticket validations, and the GTFS have respective storage capacities of 900 and 195 million kilobytes. The analysis includes: i) crossing complaints with their relevant service information from the operations database by using SQL server, ii) testing the validity of each complaint whether it is true or false, iii) for validated (true) complaints, classification analysis is conducted to identify "detection rules" that can be associated with each event, iv) building a 'troubleshooting model' for identifying undetected events in other trips and in the hope of 'debugging' them. Preliminary findings include the following detection rules: reported trips are often characterized by absent ticket validations, exceptionally short travel durations, off-track bus locations, and manual modification of departure and arrival times.

ABSTRACT. Platinum group metal-free catalysts have been considered the most promising alternative for platinum-based catalysts for the oxygen reduction reaction in fuel cells. Despite the significant advancement made in activity, their viability as fuel cell catalysts is still questionable due to their low durability. So far, deciphering the degradation mechanisms of this class of catalysts have been hampered by their undefined structure. Herein, we used a molecular model catalyst, iron-phthalocyanine, featuring Fe N4 active sites with resemblance to those in the more active Fe-N-C catalysts and studied their degradation mechanisms. Based on X-ray photoelectron spectroscopy and the electrochemical measurements, three main demetallation processes were identified: at potentials higher than 0.65V vs. RHE, where the metal center is Fe3+, an electrochemical oxidation of the ligand ring is occurring, between 0.6 and 0.2V vs. RHE, Fenton reagents are produced and attack the catalyst and support, and at lower voltages, where peroxide is produced by the catalyst and the carbon support. The combination of the different iron oxidation states together with the oxygen species directs to different degradation mechanisms. The decay rates obtained in the stability measurements establish what is mainly causing the loss of activity. Thereby, this model molecule can aid in understanding the degradation mechanisms of other platinum group metal-free oxygen reduction reaction catalysts.

ABSTRACT. Fossil fuels have dominated the world energy sector for several decades and Israel is no exception to this trend. In fact, Israeli energy production is currently far less than the annual demand, with green and renewable energy contributing less than 10% of the total production. The Israeli government has set a weighty goal of 30% of Israel’s energy coming from green and renewable sources by 2030, which puts Israel in a unique position to be a world leader in the field. The transportation sector is an exciting arena to focus on given that the sale of petrol and diesel vehicles in Israel will be banned by 2030. As such, electric vehicles are more commonly seen during daily commutes. However, to meet this stringent and aggressive target, additional smart, efficient, and clean energy alternatives are needed. Within the scope of the global clean energy landscape, hydrogen-powered fuel cells offer many advantages; clean, quiet, and reliable operation, without the recharging requirement recharging like batteries. Moreover, the use of hydrogen as a fuel translates to higher energy densities enabling longer usage times compared to batteries capable of generating the same power. As shown in the literature, anion-exchange membrane fuel cells (AEMFCs) are a strong low-cost option for the transportation industry, and, in the past few years, the use of platinum group metal (PGM) catalysts and developments in anion-exchange membranes (AEMs) have yielded striking improvements in the performance of AEMFCs at low temperatures (40-80 °C). Aside from these remarkable achievements, the literature was void of any work on AEMFCs operated at temperatures above 100 °C, despite the consensus from various models remarking that higher temperatures may yield many advantages. Interestingly, the expertise and capability to produce materials for and test AEMFCs are currently concentrated in China, USA, Japan, South Korea, Germany, Canada, France, Italy, Spain, Brazil and Israel. This gives Israel a uniquely competitive ability to be the first in many significant areas of this thriving and exciting field. As a part of my presentation, I will build upon the new field of research called high-temperature AEMFCs (HT-AEMFCs), which was pioneered during my pre-candidacy PhD studies. Herein, the results will show independent and collaborative analytical investigations into the advantages and effects of AEMFC devices operated in the temperature range of 70 – 130 °C. For instance; (i) using ultra-low quantities of PGM catalysts is an overlooked but highly practical method to potentially address the cost and durability issues of AEMFC technology, (ii) there is now a potential means to close the long-standing, existential gap between catalyst characterization and fuel cell testing techniques, and (iii) artificial intelligence can be utilized to offer accurate insights to electrochemical losses in operating fuel cells and identify possible degradation paths, and more. I am very thankful for the funding from The Israeli Smart Transportation Research Center (ISTRC) scholarship which helped me to continue my research, thereby enabling me to contribute smart and efficient techniques in the hopes of ultimately positioning Israel as a world leader in the field.

ABSTRACT. My research objective has been to help develop technologies that are cost-efficient, clean, and reliant on earth-abundant materials. The source and conservation of energy is one of the most prevalent issues in the 21st century. It is therefore important to further research and optimize electrochemical energy conversion and storage technologies which will create a green and sustainable Hydrogen economy. Most of the energy today is obtained from the combustion of fossil fuels, which produces many detrimental greenhouse gases that are released into our atmosphere. With increasing industrialization, elevated pollution levels, and peak global warming rates, it is imperative to develop alternative, cleaner technologies for energy production.

The most efficient and clean technologies today are fuel cells coupled with electrolyzer technologies, given that the energy is produced from renewable, green resources, such as wind and solar power. Fuel cells convert chemical energy, into usable electrical energy from the reaction of Hydrogen and oxygen gas. Water is the only byproduct. Electrolyzers are based on the back reaction of Fuel Cells: splitting water into Hydrogen and Oxygen gas. It also solves one of the biggest challenges towards sustaining a clean, green energy economy: energy storage. Ample amounts of energy are lost every day because there is no way to store it. This technology can use the, otherwise wasted, renewable energy and create green Hydrogen. Hydrogen is the best possible energy carrier, it has the highest energy density of all elements.

My planned studies going forward are to optimize the catalyst materials for electrolyzers. Two reactions must occur in order for the water to split: Oxygen Evolution Reaction (OER) on one side of the electorlyzers’ cell (the anode) and Hydrogen Evolution Reaction (HER) on the other side (the cathode). The HER is relatively easy to perform, leaving OER as the bottleneck of electrolyzers’ technologies. For this reaction to work with peak efficiency, precious metals are used, such as Iridium and Ruthenium. While these allow the OER to happen efficiently they are not cost efficient, these metals are scarce and therefore among the most expensive.

To switch to PGM-free materials, novel alkaline electrolyzers are being developed. The most common OER PGM-free catalysts are first-row transition metals in their oxyhydroxide forms. Specifically, NiFe oxyhydroxides have shown promising results, allowing the electrolyzer to split water at low overpotentials and high activity. I am currently working with one of the first in the world, state of the art, alkaline electrolyte membrane electrolyzer (AEMEL) test station. I am researching catalytic efficiency and long-term stability of ternary oxide materials. This can be achieved by optimizing the ratios of metals and increasing the surface area of the materials, e.g. by synthesizing aerogels. The project has already shown promising results, as the synthesis and tests of these materials are successful.

Several transition metals have been tested. Currently, the most promising being NiFeCoOx. the postulated active sight is Fe, while the Ni and Co stabilize the structure. Many ratios of each metal have also been tested and the optimal ratio has been identified.

ABSTRACT. The population of the world today keeps on growing and developing. With it, the demand for energy is increasing, along with the use of fossil fuels and emissions of greenhouse.[1] These emissions are part of the cause for global warming and climate change.[2] In order to eliminate these emissions, new energy scheme of renewable energy sources such as the "hydrogen economy" needs to be adopted.[3] In the "hydrogen economy", hydrogen is produced from excess of renewable energy and used as a chemical energy storage which can be converted back to electricity when needed, using technologies such as Proton Exchange Membrane Fuel Cells (PEMFC).[4] These devices can function as power sources for many applications such as transportation and main power.[5] In the most common PEMFC, hydrogen is oxidation reaction (HOR) takes place at the anode, and the protons it produces react with oxygen in the oxygen reduction reaction (ORR) at the cathode to produce pure water.[6] This means that fuel cells can be used as a carbon-free energy production devices. Both reactions require catalysts to increase their kinetics[6]. The cathodic reaction, the ORR, requires much larger amount of catalyst[6]. The best catalysts known today are the platinum metal group (PGM), but they are very expensive and rare, which prevent the technology from being widely used commercially. These disadvantages have led to an extensive search for efficient and inexpensive ORR catalysts. The research conducted on the development of PGM-free ORR catalysts today has been inspired from biological systems that reduce oxygen very efficiently.[7] These catalysts are usually based on macrocyclic complexes of transition metals, such as porphyrins. Although they demonstrate good catalytic activity, it is still not on-par with that of PGM catalysts[8]. Since PGM-free ORR catalysts have intrinsically low turnover frequencies, increasing the active site density may be able to compensate for this deficit.[9] Increasing the active site density can be done by using the porphyrin complexes as a precursor for aerogels, a 3D polymer with high porosity and high surface area.[10] The surface area and the pores' geometric shape and size distribution are very important feature in the catalyst and have great effect on its performance.[11] In my work, I tuned the pores of iron-porphyrin-based aerogels, by using crosslinkers in different lengths during the gel polymerization process. This change cause differences in the active site density and the mass transportation of each aerogel. Examination of the effects of these differences on the overall performance revealed that the aerogel with the most hierarchical porous structure exhibit better performance and was not necessarily correlated with the active site density. Taking these findings into account will allow better understanding of the fuel cell performance and will enable smarter development of new materials for oxygen reduction in a fuel cell. References [1] D. Carlson, IEEE Aerospace and Electronic Systems Magazine 1989, 4, 3-7. [2] D. J. Wuebbles, A. K. Jain, Fuel processing technology 2001, 71, 99-119. [3] N. P. Brandon, Z. Kurban, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2017, 375, 20160400. [4] P. Sharma, O. Pandey, in PEM Fuel Cells, Elsevier, 2022, pp. 1-24. [5] J.-H. Wee, Renewable and sustainable energy reviews 2007, 11, 1720-1738. [6] W. Daud, R. Rosli, E. Majlan, S. Hamid, R. Mohamed, T. Husaini, Renewable Energy 2017, 113, 620-638. [7] V. Artero, Nature Energy 2017, 2, 1-6. [8] G. Wu, Frontiers in Energy 2017, 11, 286-298. [9] H. A. Gasteiger, N. M. Markovic, Science 2009, 324, 48-49. [10] N. Zion, D. A. Cullen, P. Zelenay, L. Elbaz, Angewandte Chemie 2020, 132, 2504-2510. [11] S. H. Lee, J. Kim, D. Y. Chung, J. M. Yoo, H. S. Lee, M. J. Kim, B. S. Mun, S. G. Kwon, Y.-E. Sung, T. Hyeon, Journal of the American Chemical Society 2019, 141, 2035-2045.

ABSTRACT. Work-related travel is an integral part of modern society and a major source for transport externalities. The transportation preferences and habits of workers, specifically how, when, and from where to travel are strongly affected by the policies of employers, which in turn are shaped by national (tax) rules and regulations. Policies, facilities, and regulations such as parking provisions, providing a company car, or supplying public transport travel allowances are examples of employer "mobility policies" that affect employee practices. While some of these policies are considered the general norm, they are not necessarily fair (Lucas et al., 2019). Some policies tend to benefit stronger segments of society while negatively impacting the accessibility of lower-income populations to employment opportunities, directly and indirectly. Also, by shaping people’s travel patterns and residential preferences, some mobility policies might indirectly strengthen car-oriented transport development, with all of its well-known negative effects. Employer policies regarding employees' travel, therefore, clearly have many different effects on the workers and on a larger population beyond the employees. Despite the clear importance of workplace mobility policies, scant research has addressed the socially desirable behavior of businesses towards employee mobility. While employers’ role in employee mobility management is widely recognized at the practical level (see for example: (Cairns et al., 2010; Rye, 2002; Süßbauer & Schäfer, 2019), their role in shaping mobility patterns has been less studied compared to individuals or governments (Vanoutrive et al., 2010, 2012). Research objective: To empirically examine the perspectives of international corporate social responsibility (CSR) standard organizations towards employee mobility. Methodological approach: The fast-growing body of Corporate Social Responsibility (CSR)/Environmental, Social, Governance (ESG) standards and reporting frameworks can indicate what are the current expectation from businesses. The study analyzes 28 CSR/ESG voluntary and regulatory reporting frameworks, using a document analysis qualitative research. (Expected) results, conclusions and possible implications: Results show that most of the frameworks (68%) do not mention employee mobility as part of their reporting requirements. Among the frameworks that refer to employee mobility (32%), emission calculation is the main requirement: commuting and business travel, in vehicles not owned or operated by the company, are considered as upstream indirect emission (known as scope 3 of GHG protocol), while emissions from owned or operated vehicles are considered as direct emissions of the organization. Only two reporting frameworks, the GRI and the European Sustainability Reporting Standards (ESRS), place responsibility on the employer to prevent transport casualties during work-related travel and commuting where the transport has been organized by the employer (e.g. shuttels). These findings reveal a gap between the wide range of workplace mobility policies impacts, and the expectations from businesses today. The challenge of reducing transport externalities force joint effort from different stakeholders -- those must include businesses.

ABSTRACT. Introduction:

Public transport is an important aspect for any urban ecosystem, as it provides a cheap, reliable, and efficient means of transportation for people who may not have access to a car or who may not be able to afford the expenses associated with owning one. Public transport also helps to reduce traffic congestion and emissions that contribute to air pollution and climate change. By providing a reliable and accessible option, public transport also promotes social equity by ensuring that all members of society have equal access to transportation options. Furthermore, public transport can boost local economies by increasing access to employment and education opportunities, as well as by promoting local tourism. However, public transport research is still lacking in some areas, especially regarding the well-being and satisfaction of the drivers which are essential to the service quality and reliability. Drivers in public transport face challenging driving conditions that relate to the route (length, stops, road type, etc.), as well as additional characteristics unique to their line of work, such as passengers boarding and alighting, fare collection, schedule adherence, layover, etc. These conditions can affect drivers’ physical and mental health, as well as their performance and safety. However, most route design and crew scheduling processes are based on cost and time functions, as well as customer satisfaction surveys. Little attention has been paid to drivers’ characteristics as part of these processes. Drivers’ characteristics have mostly been considered only indirectly, for instance regarding makespan (the total time required to complete a set of tasks). Some recent studies have explored drivers’ satisfaction using surveys, but they did not use objective measures of drivers’ physiological states. The main challenge of this research was to integrate data from several sources, classified as exogenous and endogenous to the driver, to objectively estimate drivers’ characteristics and their impact on driving behaviour. Exogenous sources originate from vehicle, operational domain, environment, and labour contracts. Endogenous sources originate from driver’s physiological measures. The research aims to investigate and analyse the available data types for predicting driver characteristics and their impact on driving behaviour.

Methods:

The research framework consists of four steps: data collection, data merging, data analysis and data interpretation. For data collection, the following types of data were used:

* Drivers’ physiological data: collected using a medical-grade wearable compact device that offers real-time physiological data acquisition without interfering with drivers’ tasks. The device measures heart rate (HR), skin temperature (ST) and electrodermal activity (EDA), which are indicators of stress and arousal levels.

* Vehicle data: buses are commonly equipped with sensors that provide data such as speed, acceleration, braking, steering angle, and fuel consumption.

* Operational data: planned route data and schedules were collected per trip, together with actual arrival and departure times, boarding passengers’ number, and other relevant information.

The data merging process involved two steps: first, vehicle and route data were joined based on trip codes; second, physiological data was aggregated per record and based on timestamp. Event-based data was used for validation and the data was reconstructed by dividing it into 60 seconds slices if the event duration was larger than 90 seconds. For each new dataset, the physiological data were aggregated based on the new timestamp bins.

The data analysis step involved different methods to explore the relationships between the data types and the drivers’ characteristics. Machine learning (ML) models were used to predict drivers’ physical indicators (HR, ST, EDA) using only vehicle and operational data, as well as to identify the event type (stop, dwell, driving) using only physiological data. Discrete-choice models were used to estimate drivers’ satisfaction based on trip-level aggregated data and survey responses. Advanced discrete-choice models that integrate the ML insights were also developed.

Case study:

Our approach was applied in a case study with SuperBus, a public transport company in Israel, using a wearable device to collect physiological data from 12 drivers during 309 trips. The results show that our approach can aid to predict new factors. For example, it successfully predicts drivers’ physical indicators and event types using different data sources. For predicting HR, a k-nearest neighbours (k-NN) model achieved a medium correlation of R^2=0.65 and a mean squared error (MSE) of 0.024bpm. For predicting EDA, a k-NN model achieved a high correlation of R^2=0.836 and a MSE of 0.007. For predicting event type, a neural network model achieved an accuracy of 66.7% and an area under the curve (AUC) of 0.74. The results also show that our approach can estimate drivers’ satisfaction based on trip-level aggregated data and survey responses. A multilinear regression model was used to estimate drivers’ satisfaction as a function of trip duration, distance, number of stops, boarding passengers, adherence frequencies and physiological indicators. The model showed that drivers’ satisfaction was positively influenced by shorter trips, fewer stops, due to the changes in EDA and HR.

Discussion:

This study demonstrates that our new approach and framework has the potential to improve public transport planning and management by considering driver well-being and satisfaction as important factors. It can provide insights into the effects of route design and schedule adherence on drivers’ physical and mental states, as well as their preferences and choices. It can also provide feedback and alerts to drivers and fleet managers based on real-time monitoring of drivers’ conditions and events. This study also highlights some limitations and challenges, such as data availability and quality, ethical and legal issues regarding privacy and consent, and generalizability and transferability of the results to different contexts and populations.

Conclusion:

In this research we propose a novel framework for data collection, data fusion and data analysis that enables public transport planners to fully understand and address driver well-being and satisfaction when making decisions. Our approach is based on a combination of ML and discrete-choice models that integrate ML insights. The results of the case study show that the framework can predict drivers’ physical indicators using only data streams from the fleet management systems, as well as identify the event type (stop, dwell, driving) using the physiological data. This demonstrates that the framework has the potential to improve public transport planning and management by considering driver’s well-being and satisfaction as important factors. Some suggestions for future research are such as expanding the data collection to other fields and domains, improving the data analysis models with more features and variables, and validating the results with more drivers and trips.

ABSTRACT. Smart transportation systems require collaboration between algorithmic systems and human drivers. While algorithmic systems in autonomous vehicles control the vehicle, human drivers may still need to intervene in certain situations. In smart transportation services, algorithms optimize traffic flow and determine routes, but drivers must keep in mind changes in road conditions not accounted for by the algorithms (Milakis et al. 2017).

This study examines how human drivers comply with algorithmic authority while also challenging it to ensure compliance with company standards and managers’ expectations regarding safety and service quality. Early research on Algorithmic Management models, which delegate managerial authority to software applications, has primarily focused on non-traditional work arrangements like the gig economy and online labor platforms, leaving a gap in our understanding of algorithmic management's effects on driver-manager relationships in traditional employment settings (Möhlmann et al. 2021).

We refer to the implementation of algorithmic management in a traditional employment setting alongside a human manager as a hybrid algorithmic management model. This study questions how workers perceive the algorithmic supervisor, how they attribute authority to it, and whether conflicts arise from the significant differences between the algorithmic and traditional human models.

To examine these issues, we present a case study of “Bubble Dan”, a traditional transportation company that has implemented a hybrid algorithmic management model for a ride-hailing service. In this service, algorithms supervise drivers in real-time through an app that assigns riders to drivers and determines the vehicles' routes, including their stops and drivers' breaks. The app acts as a minute-to-minute monitoring tool, providing drivers with immediate feedback on their performance. Drivers in this service are employed by the company, receive a salary and employee benefits, and are assigned a human supervisory team, in contrast to comparable services like Lyft and Uber.

We were granted full access to anonymized datasets on drivers' performance by the transportation company and conducted 55 interviews with managers and drivers. Additionally, we observed the training and apprenticeship processes for 39 hours and the control room activity for 10 hours. We analyzed drivers' reports and indicators, such as passenger ranking, driving ratings, and turnover reports from the last two years, to better understand the drivers' experiences in this hybrid algorithmic management model.

Drawing on interviews with drivers and managers from a ride-hailing company, we identify three main tensions: the tension between organizational efficiency and driver flexibility, the tension between robot-like compliance and proactive responsibility, and the interplay between algorithmic judgement and human decision-making.

The first tension identified in this study is between organizational efficiency and driver flexibility in the human-algorithmic management hybrid. Organizational efficiency is a critical issue for management, as it is essential for operational success and profitability. However, the conflicting demands of both the organization and the drivers, particularly concerning breaks and the end of the shift, lead to a delicate balance between organizational efficiency and driver flexibility. The implementation of algorithmic management in a traditional organizational setting may restrict drivers' flexibility by automating their tasks and exerting control over their workday, leading to contradictory outcomes. This tension can limit drivers' autonomy over their breaks and the ability to adjust them according to their individual work pace and physical needs.

The second tension is between robot-like compliance and proactive responsibility. While the algorithmic management model allows for automation of many tasks, it has limitations, requiring drivers to exercise their judgment in additional areas and undertake extra tasks. Drivers wish to avoid taking on these extra duties, preferring to focus solely on driving without the added responsibility of compensating for the algorithm's blind spots. The management's expectation that drivers take on these additional tasks and augment the algorithm's performance creates dissatisfaction among drivers.

The third tension is the interplay between algorithmic judgement and human decision-making. Drivers and managers often encounter situations where they are unsure of how to proceed, such as determining the best routes to take or accurately evaluating driver performance. In these instances, the algorithm and human judgment can sometimes clash, creating ambiguity around the appropriate response. This challenge is compounded by the difficulty of determining when to rely on the system's decisions to automate tasks and when to exercise human judgment as a complementary measure.

Our data reveal that drivers and their human managers hold conflicting interpretations regarding the role of the algorithmic management, and these conflicts create substantial tension in the workplace, with implications for workers' performance and well-being. Looking at our tensions more broadly, we identify that the tensions result diverging expectations regarding whether the algorithm should replace humans entirely in fulfilling specific tasks or complement the human worker in their tasks, taking in input and providing feedback, yet ultimately enabling the worker to make decisions autonomously.

These findings contribute to an emerging stream of literature examining the complex interplay between automation and augmentation in organizations. While research has traditionally addressed automation and augmentation as distinct modes of algorithmic implementation (Hassani et al. 2020; Leyer and Schneider 2021; Teodorescu et al. 2021), scholars increasingly acknowledge that an organization's mode of implementing AI is typically not clearly identifiable as one or the other but positioned on a continuum between the two (Raisch and Krakowski 2021).

Our study makes a valuable contribution to the IS literature by providing what is, to our knowledge, the first empirical characterization of the dynamics of an algorithmic management model in a traditional organization framed within the context of automation and augmentation. Our work provides crucial practical insights for managers considering adopting algorithmic management models in their organizations. In particular, our findings highlight the need for organizations to provide targeted support to workers to address the tensions that stem from conflicting interpretations of the role and authority of algorithmic governance.

References Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., and Mac Feely, S. 2020. “Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?,” AI (1:2), pp. 143–155. (https://doi.org/10.3390/ai1020008). Leyer, M., and Schneider, S. 2021. “Decision Augmentation and Automation with Artificial Intelligence: Threat or Opportunity for Managers?,” Business Horizons (64:5), pp. 711–724. (https://doi.org/10.1016/j.bushor.2021.02.026). Milakis, D., van Arem, B., and van Wee, B. 2017. “Policy and Society Related Implications of Automated Driving: A Review of Literature and Directions for Future Research,” Journal of Intelligent Transportation Systems (21:4), pp. 324–348. (https://doi.org/10.1080/15472450.2017.1291351). Möhlmann, M., Zalmanson, L., Henfridsson, O., and Gregory, R. W. 2021. “Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control,” MIS Quarterly (45:4), pp. 1999–2022. (https://doi.org/10.25300/MISQ/2021/15333). Raisch, S., and Krakowski, S. 2021. “Artificial Intelligence and Management: The Automation–Augmentation Paradox,” Academy of Management Review (46:1), pp. 192–210. (https://doi.org/10.5465/amr.2018.0072). Teodorescu, M., Morse, L., Awwad, Y., and Kane, G. 2021. “Failures of Fairness in Automation Require a Deeper Understanding of Human-ML Augmentation,” MIS Quarterly (45:3), pp. 1483–1500. (https://doi.org/10.25300/MISQ/2021/16535).

ABSTRACT. Since the onset of the COVID-19 pandemic, the world has seen a dramatic increase in the prevalence of remote work, with far-reaching financial, economic, social, and environmental consequences. This has also resulted in an unprecedented expansion of the use of digital monitoring tools by employers who wish to make sure that their remotely employed workers remain productive. In this study, we create an experimental setting resembling a remote work environment, and randomly assign participants to four groups, simulating different levels of digital monitoring. We find that while the presence of monitoring both increases participants’ effort and improves their performance, a higher level of monitoring beyond the bare minimum does not. These findings have broad ramifications for both policymakers and employers looking for optimal incentives for remote workers.

ABSTRACT. This project focuses on the dilemma zone, the driver's crossing decision once the light signal turns yellow and the vehicle is driving towards an intersection at a certain speed and distance from the stop line. At the yellow signal change, drivers may find themselves too fast and close to stop safely before the stop line, or too far and slow to clear the intersection before running a red light. Although the driver's speed and distance at the time are set, factors such as the driver's temperament, their willingness to take risk or if they are rushing somewhere can come into play, making the forecasting of the driver's decision a stochastic problem.

The data for this project was collected in cooperation with a start-up company based in Israel called NoTraffic. Their solution is a camera-based traffic management platform that deals with traffic challenges. The videos from the intersections are processed in control units using video analytics methods. For this project, 10 weeks of data from 10 intersections were aquired over the course of 4 months. The new data contains the vehicles' speed, distance from stop line, lane, driving direction recognized by the units, traffic light signal and more.

Using visualization methods and logistic regression models, these vehicles are used to draw conclusions about the determining factors and probability which will lead a driver to cross or stop. These models enable us to try and forecast the probability of stopping for drivers that were caught in the crossing dilemma and to try and improve traffic safety.

ABSTRACT. Autonomous vehicles (AVs) are a disruptive mode of transportation that is rapidly evolving due to recent technological advancements in computer vision, sensor fusion, and artificial intelligence. However, despite the development of the above technologies, AVs cannot resolve each and every road scenario autonomously. Therefore, it is widely believed today that remote human assistance will be essential for AVs in the near and foreseeable future. A promising approach to provide assistance for AVs is teleoperation, which involves a remote human operator (RO) who can monitor and control the vehicle from afar. However, manually driving a vehicle remotely using a steering wheel and pedals is an extremely challenging task. Tele-assistance offers a different paradigm in which the cooperation between the human and the machine happens on the guidance level, in which the RO delegates low-level maneuvers to the AV, providing high-level instructions via a specialized interface. There may be many advantages of using tele-assistance over manually driving the vehicle, such as reducing the RO’s cognitive load, improving the AV’s safety, and potentially shortening the teleoperation session length.

To design a complete tele-assistance solution in which a teleoperator can assist a remote AV in all or at least most cases, it is necessary to first understand the different road scenarios in which remote human intervention would be required. One possible way to do this is to look at AV disengagement reports. An AV disengagement is defined as a situation in which the AV returns to manual control, or the test driver feels the need to take back the steering wheel from the AV decision system. The major source of disengagement scenario analysis is California’s Department of Motor Vehicles (CA DMV), which provides a rich publicly available dataset. However, in many cases, these reports focus on the interaction between AVs and in-vehicle test drivers, but not on interactions between AVs and remote human operators.

To bridge the above gap, we follow the user-centered design (UCD) methodology, which places the user (RO in our case) in the center of the designed system. To discover and describe the road scenarios in which an AV might need remote human assistance, we conducted in-depth semi-structured interviews with 14 experts in AV teleoperation. Then we analyzed these interviews through thematic analysis, providing a list of concrete road scenarios for AVs, classified into 10 categories and 35 sub-categories. Defining the above use cases will help lay the foundation for the definition of high-level tele-assistance commands, which will depend on what kind of maneuvers are needed. The future interface design, its components, and affordances will depend on the use-cases and problems that the teleoperator will need to solve.

ABSTRACT. Teleoperation is crucial for assisting an autonomous vehicle when it cannot make a decision, such as encountering unexpected objects or unusual situations caused by other road users. A human operator who can remotely perceive the vehicle's environment is essential in such cases. However, a major issue in teleoperation is latency, which can cause a mismatch between input commands and visual feedback and lead to stress, high cognitive workload, and decreased performance for the operator. To address this, we propose examining the effect of replacing the RGB video with a lighter, semantically segmented video between the vehicle and the teleoperation station. In our research, we examine how presenting semantically segmented driving scenes to humans affects their hazard perception and situational awareness. We compared the effects of using different levels of semantic segmentation.

ABSTRACT. Background and Research objectives In light of the increasing use of motorcycles as personal transportation, particularly in densely-populated urban areas, and the high rates of motorcycle rider injury in road accidents, there is a need to promote measures that can reduce rider injuries in accidents. One of the measures discussed in this context is the use of airbag clothing - vests/jackets by motorcycle riders (MRs). The purpose of this study was to summarize the existing knowledge in the literature regarding the effectiveness of such a measure and to assess the potential for reducing MR injury in Israel if riders were to use protective clothing with airbag technology.

Methodology The research conducted included an international literature survey on the effectiveness of MR protective clothing with airbags, an examination of data on MR injury in Israel, and an assessment of the potential contribution of this measure to reducing rider injuries. As a basis for the assessment of the potential contribution of the measure in Israel, data from two sources were analyzed: summary numbers from the National Trauma Registry and detailed records on MR casualties from the CBS accident files, for the years 2017-2021. The assessment of the potential contribution of the measure was performed using a model developed in a European study (Serre et al, 2021), but with the MR injury data in Israel. From the European study, there were three assessment scenarios with different reduction rates in MR casualties, as a result of adopting the measure. The evaluation applied an economic model that compares the benefits of the measure - the costs of saved MR casualties resulting from the use of airbag clothing, to the measure's costs - the investment in subsidizing the purchase by MRs and in publicity campaigns, whereas both components are dependent on the measure's penetration rate in the MR population. For each scenario, 20 estimates were conducted in the study, with various assumptions regarding penetration rates (between 3% and 100%), cost of the measure, and economic framework (interest rate).

Results a. Findings from the international literature In the literature survey, there were two components: a scientific literature review and a supplementary examination of the current situation through internet sites and inquiries to organizations dealing with MR safety in Europe. In the scientific literature, we found that most studies that examined the effectiveness of MR clothing with airbags were based on computer simulations of various impact configurations or laboratory experiments, while accident studies under real-world conditions were rare. Overall, the studies indicated a potential reduction in MR injuries while using airbag clothing, according to various injury metrics, but the tests were mostly conducted at low impact speeds, up to 40-50 km/h. A recent European study (Serre et al., 2021) found that the use of airbag clothing can lead to a reduction in the MR fatalities and injuries in Europe by between 1% and 19%. In the supplementary search, we found a self-reporting of the measure's users that supported the measure effectiveness in accidents. Additionally, it was noted that the devices were successfully incorporated in motorcycle races, where airbag jackets apparently contributed to reducing injury of the participants. However, regarding ordinary MRs, there were no findings showing the device’s contribution to improving rider safety in real accident conditions. Furthermore, among MRs, there was a lack of consensus regarding the use of these devices: surveys conducted in the US and Spain showed that only 28%-29% of riders supported the mandatory use. To assess the potential contribution of the measure to reducing MR injury, it is necessary to define the relevant body areas for protection by the devices and the extent of injury in such areas. According to the international literature, the relevant body area for protection is the thorax, with the common estimate for the extent of MR injury of 20%-30%, especially among serious and fatal injuries.

b. Characteristics of motorcyclist injury in Israel The National Trauma Registry showed that, on average, 1,427 MRs and 59 passengers were hospitalized annually in Israel. Among hospitalized MRs, 44% were severe injuries (according to the MAIS3+ index). The rate of thorax injuries was 28% among all hospitalized riders, and higher, at 42%, among severe casualties. Examination of MR injury cases according to the ISS scale and hospitalization characteristics (length of hospital stay, intensive care treatment, etc.) showed that when the thorax area was affected, higher severity levels were generally observed compared to cases without chest injuries. Among hospitalized MRs with thorax injuries versus others, higher shares were found in ages over 35, males, non-Jewish population, and in accidents occurring on non-urban roads. According to the CBS national files, on average per year in Israel, 63 MRs were killed, 503 were seriously injured, and 1457 were slightly injured, in road accidents. Among the leading characteristics of MR accidents were: riders' age groups of 20-24, 25-34; gender - male; population group – Jews; motorcycle size - between 126-400 cc; accident sites - urban roads, both sections and intersections, yet, among severe accidents, a significant increase in the share of non-urban sites, with a speed limit of over 50 km/h, was observed; accident types - head-side and side-side collisions, and single-vehicle accidents.

c. Assessing the potential for reducing motorcyclist injury As explained above, with each of the three scenarios developed in Europe, 20 estimations were conducted for Israeli conditions, with various assumptions regarding the penetration rate of the measure, its cost, and the economic framework. All the results showed the investment in promoting MR airbag clothing in Israel to be worthwhile: in the conservative scenario (in terms of expected injury reduction), the benefit-cost ratios were in the range of 1.0-1.3, with a feasible subsidy percentage for the device purchase up to 20%-25%. In the other two scenarios (with higher reduction factors assumed), higher benefit-cost ratios were obtained, in the ranges of 2.1-2.7 and 3.5-4.5, respectively, with a feasible subsidy percentages up to 50% and over 80%, respectively.

Conclusions and possible implications Research literature generally supports the claim that airbag clothing will be effective in reducing MR injury. However, most findings so far came from simulations or laboratory experiments. There is a need for empirical studies that examine MR injury in road accidents, comparing cases with the use of such means versus those without them. MRs in Europe, so far, do not support the obligatory use of such means due to the lack of common regulation. The current situation, i.e. the lack of findings on the device’s effectiveness in real accidents and uniform rules for their testing, reflects, as yet, an insufficient basis to promote the obligatory use of such means in Israel. However, the MR injury data in Israel indicate a significant scope of injuries to the thorax area and their high severity, while cost-benefit evaluations consistently demonstrate the expected feasibility of investing in the measure in Israel, even in the most conservative scenario. Therefore, there is room to encourage the use of airbag clothing among MRs in Israel, including subsiding of their purchase.

Reference Serre, T., Naude, C., Perrin, C., Canu, A., Breunig, S., et al. (2021). Safety and Economic Benefits, Deliverable D.6.2. Project PIONEERS - Protective Innovations of New Equipment for Enhanced Rider Safety. IFSTTAR - the French Institute of Science and Technology for Transport, Development and Networks.