Publicacións

@article{montero-lamas_bus_2026,

title = {Bus travel time prediction in mixed traffic: a multisource big data approach with multiple modelling strategies},

author = {Yaiza Montero-Lamas and Alberto Fernández-Sánchez and Marcos Gestal-Pose and Margarita Novales-Ordax and Alfonso Orro-Arcay},

url = {https://doi.org/10.1186/s12544-026-00815-3},

doi = {10.1186/s12544-026-00815-3},

issn = {1866-8887},

year  = {2026},

date = {2026-07-01},

urldate = {2026-08-17},

journal = {European Transport Research Review},

volume = {18},

number = {1},

pages = {57},

abstract = {This study examines the prediction of bus travel times within urban corridors, using an extensive dataset from transit management databases and on-street sensors. The analysis focuses on a range of variables, including corridor configuration, general traffic conditions, and intrinsic bus transit factors. Employing Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF), bus travel times are modelled across four distinct urban corridors in A Coruña, Spain, considering dynamic variables like traffic flow rate, general travel time, and average stream patronage per bus stop, with patronage showing the strongest influence on bus travel time in three of the four corridors, similar to the sum of all general traffic variables. Furthermore, these models are applied to a joint dataset encompassing all corridors, incorporating static variables such as bus stops per kilometre, and, for the first time in the field, the percentage of corridors with adjacent parking and percentage with one lane, both considered as an interaction. This allows predictions of bus travel time changes due to corridor modifications and travel times for new bus routes in unserved areas. Findings reveal that, for our dataset, none of the three ML approaches has consistently proven to be preferable in bus travel time predictions, while MLR provides competitive results, balancing accuracy and interpretability despite its flexibility constraints. The study underscores the importance of selecting models based on data range and transit context, advocating for simplicity in constrained scenarios. This developed methodology provides a valuable planning tool for transport agencies, adaptable to other urban contexts, and highlights the benefits of optimising corridor configurations to enhance bus travel time performance.},

keywords = {Bluetooth sensors, Bus travel time prediction, Inductive loops, Linear Regression, machine learning, Transit planning},

pubstate = {published},

tppubtype = {article}

}

@article{montero-lamas_spatial_2024,

title = {A spatial statistical approach to estimate bus stop demand using GIS-processed data},

author = {Yaiza Montero-Lamas and Rubén Fernández-Casal and Francisco Alberto Varela-García and Alfonso Orro-Arcay and Margarita Novales-Ordax},

url = {https://www.sciencedirect.com/science/article/pii/S0966692324001157},

doi = {10.1016/j.jtrangeo.2024.103906},

issn = {0966-6923},

year  = {2024},

date = {2024-06-01},

urldate = {2026-04-21},

journal = {Journal of Transport Geography},

volume = {118},

pages = {103906},

abstract = {This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits.},

keywords = {Bus stop demand estimation, Generalized additive model, Geospatial analysis, GIS, Spatial dependence, Transit planning},

pubstate = {published},

tppubtype = {article}

}

@article{montero-lamas_analysis_2022,

title = {Analysis of the Relationship between the Characteristics of the Areas of Influence of Bus Stops and the Decrease in Ridership during COVID-19 Lockdowns},

author = {Yaiza Montero-Lamas and Alfonso Orro-Arcay and Margarita Novales-Ordax and Francisco Alberto Varela-García},

url = {https://www.mdpi.com/2071-1050/14/7/4248},

doi = {10.3390/su14074248},

issn = {2071-1050},

year  = {2022},

date = {2022-01-01},

urldate = {2026-08-11},

journal = {Sustainability},

volume = {14},

number = {7},

pages = {4248},

abstract = {This study aimed to investigate the relationship between the characteristics of the areas of influence of bus stops and the decrease in ridership during COVID-19 lockdowns and subsequent initial reopening processes. A novel GIS methodology was developed to determine these characteristics from a large amount of data with high spatial detail and accurately assign them to individual bus stops. After processing the data, several multiple linear regression models were developed to determine the variables related to different activities and changes in mobility during lockdown that may explain the variation in demand owing to the COVID-19 pandemic. The characteristics related to population and land use were also studied. The proposed methodology can be used to improve transit planning during exceptional situations, by strengthening public transport in areas with a predictably higher transit demand, instead of uniformly decreasing the availability of public transport services, promoting sustainable mobility. The efficiency of the proposed methodology was shown by performing a case study that analysed the variation in bus demand in A Coruña, Spain. The areas with the highest sustained demand were those with low inhabitant incomes, a high population density, and significant proportions of land use dedicated to hospitals, offices, or supermarkets.},

note = {Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {bus stop patronage, COVID-19, GIS, land use, Transit planning, urban mobility},

pubstate = {published},

tppubtype = {article}

}