publications

2026

1. ERC

   

   Maps are good but are simpler maps better? Insights on urban bicycling in the US

   M. Abdalazeem, G. Altayb, E. Christofa, and J. Oke

   Environmental Research Communications, 2026

   Abs DOI Bib HTML

   This research investigates cyclist preferences for map representations and route selection criteria through a national survey study. We analyze how schematic bicycle maps can influence mode choice and reduce vehicle emissions in urban areas, identifying key design features that affect cycling behavior.

   @article{abdalazeem2026maps,
     title = {Maps are good but are simpler maps better? Insights on urban bicycling in the US},
     author = {Abdalazeem, M. and Altayb, G. and Christofa, E. and Oke, J.},
     journal = {Environmental Research Communications},
     volume = {8},
     number = {2},
     pages = {025004},
     year = {2026},
     publisher = {IOP Publishing},
     doi = {10.1088/2515-7620/ae3945},
   }

2. ERC

   

   Scenario discovery framework aids robust regional emissions mitigation planning

   P. Zhao, J. Mega, M. Abdalazeem, M. Arabi, C. V. Barchers, and J. Oke

   Environmental Research Communications, 2026

   Abs DOI Bib HTML

   This study applies scenario discovery to identify robust decarbonization pathways for regional transportation planning, analyzing multiple intervention strategies and their effectiveness across different future scenarios.

   @article{zhao2026scenario,
     title = {Scenario discovery framework aids robust regional emissions mitigation planning},
     author = {Zhao, P. and Mega, J. and Abdalazeem, M. and Arabi, M. and Barchers, C. V. and Oke, J.},
     journal = {Environmental Research Communications},
     volume = {8},
     number = {3},
     pages = {031008},
     year = {2026},
     publisher = {IOP Publishing},
     doi = {10.1088/2515-7620/ae4b94},
   }

3. DST

   

   Explainable Data-Driven Multi-Filter Framework for Bot Detection in Incentivized Online Surveys

   A. Ruger, M. Abdalazeem, E. Christofa, and J. Oke

   Data Science for Transportation, 2026

   In press

   Abs Bib

   This paper develops a multi-filter bot detection pipeline combining platform signals, browser fingerprinting, timing analysis, and XGBoost validation for quality control in incentivized online survey data collection.

   @article{ruger2026bot,
     title = {Explainable Data-Driven Multi-Filter Framework for Bot Detection in Incentivized Online Surveys},
     author = {Ruger, A. and Abdalazeem, M. and Christofa, E. and Oke, J.},
     journal = {Data Science for Transportation},
     year = {2026},
     note = {In press},
   }

4. 

   Toward Unified Design Rules for Schematic Bicycle Maps

   M. Abdalazeem, E. Christofa, C. V. Barchers, and J. Oke

   2026

   Under review

   Abs

   This paper synthesizes evidence from survey research and behavioral studies to propose unified design rules for schematic bicycle maps that improve usability and promote cycling adoption.

5. 

   Schematic maps promise to induce micromobility ridership: a Bayesian latent class ordered logistic approach

   M. Abdalazeem, A. Ruger, E. Christofa, and J. Oke

   2026

   Under review

   Abs

   Using a Bayesian latent class ordered logistic regression framework, this study identifies heterogeneous rider groups and models stated intentions to use micromobility under different map design conditions.

2025

1. DST

   

   A Roadway Crash Typology of Census Tracts Enables Targeted Interventions via Interpretable Machine Learning

   M. Abdalazeem and J. Oke

   Data Science for Transportation, 2025

   Abs DOI Bib HTML

   This study applies UMAP and Gaussian mixture models to classify 2,480 census tracts into crash typologies using state-level crash records in New England. Identified distinct spatial patterns in crash frequency and severity linked to roadway characteristics and socioeconomic factors. Built predictive models to forecast crash risk by typology, enabling targeted safety interventions.

   @article{abdalazeem2025crash,
     title = {A Roadway Crash Typology of Census Tracts Enables Targeted Interventions via Interpretable Machine Learning},
     author = {Abdalazeem, M. and Oke, J.},
     journal = {Data Science for Transportation},
     volume = {7},
     pages = {14},
     year = {2025},
     publisher = {Springer},
     doi = {10.1007/s42421-025-00128-2},
   }

2. 

   A novel origin-destination-transfer model using mobile ticketing activations with seasonal passenger typology and spatial error correction

   M. Abdalazeem, T. Oke, and J. Oke

   2025

   Under review

   Abs

   This paper presents an advanced ODX model integrating trip chaining, passenger typology, and spatial error correction to infer complete trip patterns, including transfers, in a regional transit system. The model leverages iterative proportional fitting (IPF) and machine learning techniques to scale partial AFC-based OD estimates to the full transit network population.

2024

1. DST

   

   Enhanced Seasonal Typology-Informed Transit Trip Chaining via Mobile Boarding and Survey Data

   M. Abdalazeem and J. Oke

   Data Science for Transportation, 2024

   Abs DOI Bib HTML

   This study develops a trip chaining model incorporating passenger typology and seasonal variations to infer full passenger journeys using boarding-only AFC data. The model integrates a gradient boosting machine (GBM) for spatial error correction, reducing mean absolute error (MAE) by 70% and symmetric mean absolute percentage error (SMAPE) by 85%.

   @article{abdalazeem2024enhanced,
     title = {Enhanced Seasonal Typology-Informed Transit Trip Chaining via Mobile Boarding and Survey Data},
     author = {Abdalazeem, M. and Oke, J.},
     journal = {Data Science for Transportation},
     volume = {6},
     pages = {19},
     year = {2024},
     publisher = {Springer},
     doi = {10.1007/s42421-024-00108-y},
   }

2023

1. DST

   

   Extracting Spatiotemporal Bus Passenger Trip Typologies from Noisy Mobile Ticketing Boarding Data

   M. Abdalazeem and J. Oke

   Data Science for Transportation, 2023

   Abs DOI Bib HTML

   This paper develops a novel clustering framework using hierarchical clustering and dynamic time warping (DTW) to identify distinct spatiotemporal passenger typologies in the Pioneer Valley Transit Authority (PVTA) bus system. The method analyzes activation time series data from mobile ticketing transactions to infer behavioral patterns of different passenger groups.

   @article{abdalazeem2023extracting,
     title = {Extracting Spatiotemporal Bus Passenger Trip Typologies from Noisy Mobile Ticketing Boarding Data},
     author = {Abdalazeem, M. and Oke, J.},
     journal = {Data Science for Transportation},
     volume = {5},
     pages = {20},
     year = {2023},
     publisher = {Springer},
     doi = {10.1007/s42421-023-00082-x},
   }

2. IJTST

   

   Origin-destination inference in public transportation systems: A comprehensive review

   M. Abdalazeem and J. Oke

   International Journal of Transportation Science and Technology, 2023

   Abs DOI Bib HTML

   This paper presents a comprehensive review of state-of-the-art methods for origin-destination estimation in transit networks, identifying key advancements, data sources, and modeling techniques. The review synthesizes research trends in OD modeling, emphasizing the transition from survey-based methods to big data-driven techniques utilizing automated fare collection, automatic vehicle location, and mobile ticketing data.

   @article{abdalazeem2023origin,
     title = {Origin-destination inference in public transportation systems: A comprehensive review},
     author = {Abdalazeem, M. and Oke, J.},
     journal = {International Journal of Transportation Science and Technology},
     volume = {12},
     number = {1},
     pages = {315--328},
     year = {2023},
     publisher = {Elsevier},
     doi = {10.1016/j.ijtst.2022.03.002},
   }

2020

1. MATEC

   

   Khartoum sky trains network: A study on implementing caterpillar trains concept in Khartoum

   M. A. Abdalazeem, O. Mohammed, M. Ibrahim, and H. G. Osman

   MATEC Web of Conferences, 2020

   Best Project Award from the Civil Engineering Department Abs DOI Bib HTML

   Best Project Award

   This study develops a feasibility study for implementing a caterpillar-style elevated light rail system to alleviate traffic congestion in developing cities, using Khartoum, Sudan, as a case study. The research includes route optimization analysis using GIS and evaluation of financial and environmental impacts.

   @article{abdalazeem2020khartoum,
     title = {Khartoum sky trains network: A study on implementing caterpillar trains concept in Khartoum},
     author = {Abdalazeem, M. A. and Mohammed, O. and Ibrahim, M. and Osman, H. G.},
     journal = {MATEC Web of Conferences},
     volume = {308},
     pages = {02003},
     year = {2020},
     publisher = {EDP Sciences},
     doi = {10.1051/matecconf/202030802003},
   }