Traffic Operations and Management

Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for Mid-term Human Mobility Prediction

Huang, Zongyuan
Wang, Weipeng
Huang, Shaoyu
Gonzalez, Marta C.
Jin, Yaohui
Xu, Yanyan
2025

Predicting individual mobility patterns is crucial across various applications. While current methods mainly focus on predicting the next location for personalized services like recommendations, they often fall short in supporting broader applications such as traffic management and epidemic control, which require longer period forecasts of human mobility. This study addresses mid-term mobility prediction, aiming to capture daily travel patterns and forecast trajectories for the upcoming day or week. We propose a novel Multi-scale Spatial-Temporal Decoupled Predictor (MSTDP) designed to...

Unraveling environmental justice in ambient PM2.5 exposure in Beijing: A big data approach

Xu, Yanyan
Jiang, Shan
Li, Ruiqi
Zhang, Jiang
Zhao, Jinhua
Abbar, Sofiane
González, Marta C.
2019

Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then...

Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram

Ambühl, Lukas
Menendez, Monica
González, Marta C.
2023

The science of cities aims to model urban phenomena as aggregate properties that are functions of a system’s variables. Following this line of research, this study seeks to combine two well-known approaches in network and transportation science: (i) The macroscopic fundamental diagram (MFD), which examines the characteristics of urban traffic flow at the network level, including the relationship between flow, density, and speed. (ii) Percolation theory, which investigates the topological and dynamical aspects of complex networks, including traffic networks. Combining these two approaches,...

Modeling Urban Air Quality Using Taxis as Sensors

Noulas, Anastasios
Acikmese, Yasin
LI, Charles QC
Patel, Milan Y.
Babul, Shazia Ayn
Cohen, Ronald C.
Lambiotte, Renaud
Gonzalez, Marta C.
2025

Monitoring urban air quality with high spatiotemporal resolution continues to pose significant challenges. We investigate the use of taxi fleets as mobile sensing platforms, analyzing over 100 million PM2.5 readings from more than 3,000 vehicles across six major U.S. cities during one year. Our findings show that taxis provide fine-grained, street-level air quality insights while ensuring city-wide coverage. We further explore urban air quality modeling using traffic congestion, built environment, and human mobility data to predict pollution variability. Our results highlight geography-...

Macroscopic dynamics and the collapse of urban traffic

Olmos, Luis E.
Çolak, Serdar
Shafiei, Sajjad
Saberi, Meead
González, Marta C.
2018

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of...

Impact of navigation apps on congestion and spread dynamics on a transportation network

Bagabaldo, Alben Rome
Gan, Qianxin
Bayen, Alexandre M.
González, Marta C.
2024

In recent years, the widespread adoption of navigation apps by motorists has raised questions about their impact on local traffic patterns. Users increasingly rely on these apps to find better, real-time routes to minimize travel time. This study uses microscopic traffic simulations to examine the connection between navigation app use and traffic congestion. The research incorporates both static and dynamic routing to model user behavior. Dynamic routing represents motorists who actively adjust their routes based on app guidance during trips, while static routing models users who stick to...

Imitate the Right Data: City-wide Mobility Generation with Graph Learning

Wu, Jiaman
Cao, Shangqing
Perona, Giuseppe
Gonzalez, Marta C.
2024

Realistic simulation of massive human mobility data aids in a range of applications such as traffic management, public transport optimization, and emergency response planning. This task is challenging as most trajectory datasets are sparse. When used for city-level planning, models that merely mimic the sparse input dataset from mobile phones induce errors. In this work, we emphasize the importance of validating simulations with complete trajectories. We present a model for city-wide human mobility generation from sparse data. We first extract spatial, temporal, and activity features from...

Estimating MFDs, trip lengths and path flow distributions in a multi-region setting using mobile phone data

Paipuri, Mahendra
Xu, Yanyan
González, Marta C.
Leclercq, Ludovic
2020

The present work proposes a global framework to estimate all MFD model parameters using mobile phone data. The three major components that are estimated in the present context are MFD shapes, regional trip lengths and path flow distribution. A trip enrichment scheme based on the map matching process is proposed for the trips that have sparser records. Time dependent penetration rates are estimated by fusing the OD matrix and the Loop Detector Data (LDD). Two different types of penetration rates of vehicles are proposed based on the OD flow and the trips starting within an origin,...

Deconstructing laws of accessibility and facility distribution in cities

Xu, Yanyan
Olmos, Luis E.
Abbar, Sofiane
González, Marta C.
2020

The era of the automobile has seriously degraded the quality of urban life through costly travel and visible environmental effects. A new urban planning paradigm must be at the heart of our road map for the years to come, the one where, within minutes, inhabitants can access their basic living needs by bike or by foot. In this work, we present novel insights of the interplay between the distributions of facilities and population that maximize accessibility over the existing road networks. Results in six cities reveal that travel costs could be reduced in half through redistributing...

Big Data Fusion to Estimate Urban Fuel Consumption: A Case Study of Riyadh

Kalila, Adham
Awwad, Zeyad
Di Clemente, Riccardo
González, Marta C.
2018

Falling oil revenues and rapid urbanization are putting a strain on the budgets of oil-producing nations, which often subsidize domestic fuel consumption. A direct way to decrease the impact of subsidies is to reduce fuel consumption by reducing congestion and car trips. As fuel consumption models have started to incorporate data sources from ubiquitous sensing devices, the opportunity is to develop comprehensive models at urban scale leveraging sources such as Global Positioning System (GPS) data and Call Detail Records. This paper combines these big data sets in a novel method to model...