A methodology to develop a geospatial transportation typology

Popovich, Natalie; Spurlock, C. Anna; Needell, Zachary; Jin, Ling; Wenzel, Tom; Sheppard, Colin J. R.; Asudegi, Mona

doi:10.1016/j.jtrangeo.2021.103061

Cited by 6 publications

(1 citation statement)

References 41 publications

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“…Freeway segments (FRCs 1 and 2), which generally account for less than 3% of the total lane miles in urban tracts as defined in the transportation typology [24], are excluded in the aggregation to avoid the influence of higher speed and volume from these non-typical road types in urban areas.…”

Section: ) Network-level Flow and Density Aggregation At Urban Census...mentioning

confidence: 99%

Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Jin,

Xu,

Wang

et al. 2023

2023 IEEE International Conference on Big Data (BigData)

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Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level.

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Section: ) Network-level Flow and Density Aggregation At Urban Census...mentioning

confidence: 99%

Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Jin,

Xu,

Wang

et al. 2023

2023 IEEE International Conference on Big Data (BigData)

Self Cite

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Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Jin,

Xu,

Wang

et al. 2024

Data Sci. Transp.

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The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

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Inequality and the future of electric mobility in 36 U.S. Cities: An innovative methodology and comparative assessment

Romero‐Lankao

Wilson²,

Zimny-Schmitt³

2022

Energy Research & Social Science

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A methodology to develop a geospatial transportation typology

Cited by 6 publications

References 41 publications

Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Inequality and the future of electric mobility in 36 U.S. Cities: An innovative methodology and comparative assessment

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