publications | Mohammed Abdalazeem

Journal Articles (Published)

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

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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}, volume = {8}, pages = {21}, year = {2026}, publisher = {Springer}, doi = {10.1007/s42421-026-00158-4}, }
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

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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}, }
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

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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}, }
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

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

M. Abdalazeem and J. Oke

Data Science for Transportation, 2024

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

M. Abdalazeem and J. Oke

Data Science for Transportation, 2023

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

M. Abdalazeem and J. Oke

International Journal of Transportation Science and Technology, 2023

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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}, }

Conference Papers

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

In MATEC Web of Conferences, 2020

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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.
@inproceedings{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.}, booktitle = {MATEC Web of Conferences}, volume = {308}, pages = {02003}, year = {2020}, publisher = {EDP Sciences}, doi = {10.1051/matecconf/202030802003}, }

Under Review

Toward Unified Design Rules for Schematic Bicycle Maps

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

2026

Under review

Abs Bib

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.
@article{abdalazeem2026designrules, title = {Toward Unified Design Rules for Schematic Bicycle Maps}, author = {Abdalazeem, M. and Christofa, E. and Barchers, C. V. and Oke, J.}, year = {2026}, note = {Under review}, }
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 Bib

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.
@article{abdalazeem2026ridership, title = {Schematic maps promise to induce micromobility ridership: a Bayesian latent class ordered logistic approach}, author = {Abdalazeem, M. and Ruger, A. and Christofa, E. and Oke, J.}, year = {2026}, note = {Under review}, }

Three-decade systematic review highlights enduring barriers to cycling and reveals rural–urban evidence gap

N. Bohlke, H. Bell, M. Abdalazeem, J. Oke, and E. Christofa

2026

Under review

Bib

@article{bohlke2026barriers,
  title = {Three-decade systematic review highlights enduring barriers to cycling and reveals rural--urban evidence gap},
  author = {Bohlke, N. and Bell, H. and Abdalazeem, M. and Oke, J. and Christofa, E.},
  year = {2026},
  note = {Under review}
}

In Preparation

A Unified Geospatial Data Pipeline and Detection Algorithm for Bicycle Routes from Heterogeneous Network Data

M. Abdalazeem, J. Demers, E. Christofa, and J. Oke

In preparation

Bib

@article{abdalazeem2026pipeline,
  title = {A Unified Geospatial Data Pipeline and Detection Algorithm for Bicycle Routes from Heterogeneous Network Data},
  author = {Abdalazeem, M. and Demers, J. and Christofa, E. and Oke, J.},
  note = {In preparation},
}

Automated Schematization of Bicycle Route Networks via Mixed-Integer Optimization

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

In preparation

Bib

@article{abdalazeem2026schematization,
  title = {Automated Schematization of Bicycle Route Networks via Mixed-Integer Optimization},
  author = {Abdalazeem, M. and Christofa, E. and Oke, J.},
  note = {In preparation},
}

Road Network Typology of US Metropolitan Areas Explains Congestion, Mobile Emissions, and Safety Outcomes

P. Zhao, A. Weinman, V. Rost-Nasshan, M. Abdalazeem, Z. Han, M. Arabi, and J. Oke

In preparation

Bib

@article{zhao2026roadnetwork,
  title = {Road Network Typology of US Metropolitan Areas Explains Congestion, Mobile Emissions, and Safety Outcomes},
  author = {Zhao, P. and Weinman, A. and Rost-Nasshan, V. and Abdalazeem, M. and Han, Z. and Arabi, M. and Oke, J.},
  note = {In preparation},
}

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

In preparation

Abs Bib

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.
@article{abdalazeem2025novel, title = {A novel origin-destination-transfer model using mobile ticketing activations with seasonal passenger typology and spatial error correction}, author = {Abdalazeem, M. and Oke, T. and Oke, J.}, note = {In preparation}, }