Kandó Kálmán Doctoral School of Transportation and Vehicle Engineering  

BME KJK, Department of Transport Technology and Economics

Supervisor: Dr. CSONKA Bálint

Multidimensional Modeling and Integration of Sustainable Micro-Mobility in Urbanized Context

Introducing the research area

In recent years, the emergence of shared electric micro-mobility (SEMM) has further contributed to sustainable transportation options. SEMM systems, in particular, offer versatile and convenient commuting solutions, especially in dense urban areas with heavy traffic. These systems not only provide a practical alternative to private vehicles but also help reduce air pollution since they produce zero emissions. Additionally, SEMM promotes a healthy lifestyle by encouraging individuals to incorporate biking into their daily routines, thereby improving overall fitness. Overall, the integration of SEMM systems presents an exciting opportunity to enhance sustainable mobility, reduce emissions, improve public health, and create more livable cities. This research will contribute to the existing body of knowledge by exploring the multi-dimensional aspects of sustainable micro-mobility integration and proposing practical strategies for its implementation.

 

Brief introduction of the research place

 

My research is carried out at the Department of Transport and Economics at BME, specifically, in the Electromobility Research Group led by Prof. Csaba Csiszár. The group's purpose is to integrate expertise in transportation engineering, electrical engineering, energetics, and social sciences with a holistic view. The research group closely cooperates with the leading transportation stakeholders and research centers in the field.

 

History and context of the research

This literature review focuses on identifying gaps in existing research on cycling, bike-sharing, and shared electric micro-mobility. Several areas have been identified where further investigation is warranted. Firstly, previous studies have examined the causes of bike accidents [1]–[3] but have not thoroughly explored the estimation of injury severity based on factors such as roadway characteristics, road surface conditions, land use, transportation networks, and the built environment. Secondly, the influence of weather conditions on usage patterns of different user groups in bike-sharing has been overlooked, with most studies concentrating on overall users [4]–[7]. Thirdly, a comprehensive exploration of bike-sharing dynamics requires the inclusion of various factors such as touristic points of interest, crossings, traffic signals, transit stops, educational and healthcare facilities, and other environmental elements that have been frequently disregarded in prior studies [8]–[11]. Moreover, research on micro-mobility mode choice has often focused on specific modes or neglected crucial factors like time parameters and cost in assessing preferences [12]–[16]. Lastly, while factors influencing the location of shared electric micro-mobility stations have been identified [17]–[22], a novel approach utilizing the Analytic Hierarchy Process (AHP) considering both criteria and sub-criteria is necessary to optimize station placement accurately. Addressing these gaps will contribute to a more comprehensive understanding of these areas and provide valuable insights for future research in the field of cycling, bike-sharing, and shared electric micro-mobility.

 

The research goals, open questions

Considering the reviewed work, this research aims to achieve several goals. Firstly, it seeks to investigate the causes and severity levels of bike accidents, focusing on the estimation of injury severity probabilities based on roadway characteristics and road surface conditions. Additionally, the research aims to understand how weather conditions influence the usage patterns of various bike-sharing user groups and compare different prediction methods for bike-sharing use, specifically considering different user groups. Moreover, it aims to incorporate a comprehensive set of variables into the analysis of bike-sharing dynamics, including touristic points of interest, crossings, traffic signals, and more. Furthermore, the research explores the preferences and interrelationships among shared electric micro-mobility modes, examining the impact of time parameters and cost on mode choices. Lastly, the research aims to optimize shared electric micro-mobility station locations in cities using the Analytic Hierarchy Process (AHP) and considering both factors and sub-criteria.

 

Methods

 

Through this research, several statistical models have been developed in different aspects. For the severity of cycling crashes, binomial, and geographically weighted regressions (GWRs) are used to develop the models. In regard to the temporal behavior of bike-sharing users, time-series models (ARIMA), count models, and tree technique models have been used. Going towards the spatial behavior of the bike-sharing users, ordinary least squares models, and geographically weighted regression are conducted. With respect to the integration of electric shared micro-mobility, discrete choice modeling has been conducted to study and compare the conventional modes with the electric and shared modes. For policy investigation, both AHP and scoring methods are used to know what factors affect the location of electric shared micro-mobility stations and which cities in Hungary are more ready to deploy such systems.

 

Results

 

Cycling Crashes Severity Models

Probabilities of major injuries are higher in signalized intersections, inclined topographies, one-way roads, urban areas, areas with higher speed limits, and during the daytime, which require more attention and better consideration. Additionally, the built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crashes index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The research also shows that commercial activity and public transport stops have a higher impact on bike crashes in the northern and western districts. The GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists.

Figure 1: Spatial distribution of the regression coefficients estimated for industrial areas.

 

Bike-Sharing Temporal Behavior Models

Negative Binomial, Poisson Regression, and Time Series models were elaborated considering the weather to reveal the differences between the members, occasional users, and visitor bike-sharing user groups. The negative Binomial approach is found to be superior to Poisson. Weather effects were varied in their influence on bike-sharing user classifications. In general, good weather conditions lead to more usage of bike-sharing. Weekends attract more occasional users and visitors than weekdays. In time series models, the seasonal trend of bike-sharing trips conducted by members was predicted without weather impact. According to the comparison, Random Forest performed better than SARIMA when the number of observations was low. Visitors are more influenced by temperature, wind, and type of day. Occasional users are more subjected to precipitation. For members, it is found that the temperature, and type of day are the most significant factors. The least factors for all are varied as well: precipitation for visitors, humidity for occasional users, precipitation, and wind for members.

 

Bike-Sharing Spatial Behavior Models

It is found that touristic points of interest, healthcare, and educational points have a positive impact on bike-sharing destinations. Public transportation stops of buses, rails, and trams are attracting bike-sharing users which has a potential for the bike-and-ride system. Land use has different effects on bike-sharing trip destinations; mostly as a circular shape variation within the urban structure of the city, such as residential, industrial, commercial, and educational zones. Other variables such as road length and water areas are forming as constraints to the bike-sharing trip destinations. The geographically weighted and spatial regression performs better than count models and random forest.

Figure 2: Spatial distribution of the regression coefficients estimated for residential areas.

 

Discrete Choice Modelling

Multinomial Logit (MNL) model is applied where a transport choice model is developed. The effect of several factors on the preferences of people toward the three micro-transport modes is evaluated. The developed transport choice model includes trip time, trip cost, walking distance, parking characteristics, and socio-demographic factors. The results indicate that travelers prefer using bikes more than e-bikes and e-scooters. Furthermore, it is found that e-scooters are the least favored by travelers. It is noteworthy that car drivers, individuals with access to or frequent usage of micro-mobility, graduate students, full-time workers, males, and young people are more willing to use shared electric micro-mobility services. The probability of choosing a transport mode based on the changes in parking type attributes is estimated in this research. The results show that travelers prefer free-floating parking when they use shared electric micro-mobility services. This research underscores the significance of parking type (docks or dockless) and socio-demographic variables when it comes to micro-mobility modes in urban areas.

 

Figure 3: The predictive margins of the model at a 20% increment in the trip cost, trip time, and walking time of shared conventional bikes.

 

Location of SEMM Stations

The examined criteria are proximity to public transportation, accessibility to key destinations, demographics, safety, land use, and pedestrian and cyclist infrastructure. Using the AHP model, the importance and ranking of each criterion were established. Results found that the most important criteria were the availability and quality of sidewalks and bike lanes in the surrounding area, and the proximity to popular destinations such as shopping centers and tourist attractions.

 

Cities' Readiness to Deploy SEMM Systems

By assessing 25 indicators related to infrastructure, safety, demographics, legislation, and transportation systems, we provide a comprehensive understanding of each city's current situation and readiness level. The analysis reveals that certain cities, such as Budapest and Gyor, are better prepared for sustainable transportation than others. However, every city has both positive and negative aspects that need to be considered. The establishment of infrastructure for cycling and connectivity to public transportation systems should be given top priority in Hungarian cities.

 

Expected impact and further research

 

The results of the research have significant potential for policymakers, urban planners, and researchers interested in promoting sustainable mobility. The developed models have been published in highly cited international journals (Q1, and Q2), indicating the importance of the research topic. The developed methods for the discrete choice modeling, and the policies research (location of stations, and city readiness) are under review in Q1, and Q2 journals, as well.

My future research is focused on the electrification of micro-mobility, the integration with autonomous vehicles, as well as, going towards the rural areas to generalize the results, and compare the effects.

 

 

 

 

Publications, references, links

List of corresponding own publications.

[P1] Jaber, A., Juhász, J., & Csonka, B. (2021). An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model. Sustainability, 13(12), 6945. MDPI AG. Retrieved from http://dx.doi.org/10.3390/su13126945 (IF=3.9)

[P2] Jaber, A., & Csonka, B. (2023). How Do Land Use, Built Environment, and Transportation Facilities Affect Bike-Sharing Trip Destinations? Promet – Traffic&Transportation, 35(1), 119–132. https://doi.org/10.7307/ptt.v35i1.67 (IF=0.9)

[P3] Jaber, A. & Csonka, B. (2023). Investigating the Temporal Differences among Bike-Sharing Users through Comparative Analysis based on Count, Time Series, and Data Mining Models. Alexandria Engineering Journal. (Accepted, IF=6.6)

[P4] Jaber, A., Abu Baker, L., & Csonka, B. (2022). The Influence of Public Transportation Stops on Bike-Sharing Destination Trips: Spatial Analysis of Budapest City. Future Transportation, 2(3), 688–697. https://doi.org/10.3390/futuretransp2030038

[P5] Jaber, A., Al-Sahili, K., Juhász, J. (2023). Demand-responsive Users’ Travel Behavior and Satisfaction Analysis in Small Cities: Case Study of the Public Transportation System in Palestine. Periodica Polytechnica Transportation Engineering, 51(2), pp. 190–199. https://doi.org/10.3311/PPtr.19914 (CS=3.3)

[P6] Jaber, A. and Al-Sahili, K. (2023). Severity of Pedestrian Crashes in Developing Countries: Analysis and Comparisons Using Decision Tree Techniques. SAE Int. J. Trans. Safety 11(2). https://doi.org/10.4271/09-11-02-0008. (CS=1.0)

[P7] Hamadneh, J., Jaber, A. (2023). Modeling of intra-city transport choice behaviour in Budapest, Hungary. Journal of Urban Mobility, 3, December 2023, 100049. https://doi.org/10.1016/j.urbmob.2023.100049

[P8] Jaber, A., Csonka, B. (2022). Temporal Travel Demand Analysis of Irregular Bike-Sharing Users. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2022. Lecture Notes in Computer Science, vol 13335. Springer, Cham. https://doi.org/10.1007/978-3-031-04987-3_35

[P9] Jaber, A., Csonka, B. and Juhász, J. (2022). Long-Term Time Series Prediction of Bike Sharing Trips: A Cast Study of Budapest City. Smart City Symposium Prague (SCSP), Prague, Czech Republic, pp. 1–5, 10.1109/SCSP54748.2022.9792540

[P10] Jaber, A., Juhász, J. (2022). Measuring and Forecasting of Passengers Modal Split Through Road Accidents Statistical Data. In: Sierpiński, G. (eds) Intelligent Solutions for Cities and Mobility of the Future. TSTP 2021. Lecture Notes in Networks and Systems, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-91156-0_2

 

Under Review papers:

[1] Towards a Sustainable and Safe Future: Mapping Bike Accidents in Urbanized Context (Safety, CS=3.3)

[2] Assessment of Hungarian large cities' readiness in adopting electric bike sharing system (ENVI, IF=4.1)

[3] The Preferences of Shared Micro-Mobility Users in Urban Areas (IEEE Access, IF=3.4)

[4] Demographic Analysis of Active Travel Users in Urban Context (IET Smart Cities, CS=6.4)

 

List of references.

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[6]               M. Azimi, L. Zhou, and Y. Qi, “Exploring the Impact of Infrastructure on Bike Sharing System Performance in Houston City.” Center for Advanced Multimodal MobilitySolutions and Education, Charlotte, 2021. [Online]. Available: https://rosap.ntl.bts.gov/view/dot/58271

[7]     T. S. Kim, W. K. Lee, and S. Y. Sohn, “Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects,” PLOS ONE, vol. 14, no. 9, 2019, doi: 10.1371/journal.pone.0220782.

[8]     S. Guidon, D. J. Reck, and K. Axhausen, “Expanding a(n) (electric) bicycle-sharing system to a new city: Prediction of demand with spatial regression and random forests,” J. Transp. Geogr., vol. 84, no. April 2020, p. 102692, 2020, doi: 10.1016/j.jtrangeo.2020.102692.

[9]     A. Faghih-Imani, N. Eluru, A. M. El-Geneidy, M. Rabbat, and U. Haq, “How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal,” J. Transp. Geogr., vol. 41, no. December 2014, pp. 306–314, 2014, doi: 10.1016/j.jtrangeo.2014.01.013.

[10]     S. Munira and I. N. Sener, “A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas,” J. Transp. Geogr., vol. 88, no. October 2020, p. 102865, 2020, doi: 10.1016/j.jtrangeo.2020.102865.

[11]     D. Zhao, G. P. Ong, W. Wang, and X. J. Hu, “Effect of built environment on shared bicycle reallocation: A case study on Nanjing, China,” Transp. Res. Part Policy Pract., vol. 128, no. October 2019, pp. 73–88, 2019, doi: 10.1016/j.tra.2019.07.018.

[12]     D. J. Reck and K. W. Axhausen, “Who uses shared micro-mobility services? Empirical evidence from Zurich, Switzerland,” Transp. Res. Part Transp. Environ., vol. 94, p. 102803, 2021, doi: 10.1016/j.trd.2021.102803.

[13]     D. J. Reck, H. Martin, and K. W. Axhausen, “Mode choice, substitution patterns and environmental impacts of shared and personal micro-mobility,” Transp. Res. Part Transp. Environ., vol. 102, no. January 2022, p. 103134, 2022, doi: 10.1016/j.trd.2021.103134.

[14]     B. Kutela, C. Mbuya, S. Swai, D. Imanishimwe, and N. Langa, “Associating stated preferences of emerging mobility options among Gilbert City residents using Bayesian Networks,” Cities, vol. 131, p. 104064, 2022, doi: 10.1016/j.cities.2022.104064.

[15]     S. Rayaprolu and M. Venigalla, “Motivations and Mode-choice Behavior of Micromobility Users in Washington, DC,” J. Mod. Mobil. Syst., vol. 1, pp. 110–118, 2020, doi: 10.13021/jmms.2020.2894.

[16]     H. Younes, Z. Zou, J. Wu, and G. Baiocchi, “Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C,” Transp. Res. Part Policy Pract., vol. 134, pp. 308–320, 2020, doi: 10.1016/j.tra.2020.02.021.

[17]     B. Kutela, N. Novat, E. K. Adanu, E. Kidando, and N. Langa, “Analysis of residents’ stated preferences of shared micro-mobility devices using regression-text mining approach,” Transp. Plan. Technol., vol. 45, no. 2, pp. 159–178, 2022, doi: 10.1080/03081060.2022.2089145.

[18]     A. Shaer and H. Haghshenas, “The impacts of COVID-19 on older adults’ active transportation mode usage in Isfahan, Iran,” J. Transp. Health, vol. 23, p. 101244, 2021, doi: 10.1016/j.jth.2021.101244.

[19]     A. Li, J. Chen, T. Qian, W. Zhang, and J. Wang, “Spatial Accessibility to Shopping Malls in Nanjing, China: Comparative Analysis with Multiple Transportation Modes,” Chin. Geogr. Sci., vol. 30, pp. 710–724, 2020, doi: 10.1007/s11769-020-1127-y.

[20]     R. L. Abduljabbar, S. Liyanage, and H. Dia, “The role of micro-mobility in shaping sustainable cities: A systematic literature review,” Transp. Res. Part Transp. Environ., vol. 92, p. 102734, 2021, doi: 10.1016/j.trd.2021.102734.

[21]     L. Liu and H. J. Miller, “Measuring the impacts of dockless micro-mobility services on public transit accessibility,” Comput. Environ. Urban Syst., vol. 98, p. 101885, 2022, doi: 10.1016/j.compenvurbsys.2022.101885.

[22]     M. Dozza, A. Violin, and A. Rasch, “A data-driven framework for the safe integration of micro-mobility into the transport system: Comparing bicycles and e-scooters in field trials,” J. Safety Res., vol. 81, pp. 67–77, 2022, doi: 10.1016/j.jsr.2022.01.007.