Understanding Road Usage Patterns in Urban Areas

By Pu Wang, Timothy Hunter, Alexandre M. Bayen, K. Schechtner, Marta C. González. Scientific Reports, 2012

408 citations recorded by the source corpus.

In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.

Subfields: Transportation

View Paper: https://www.semanticscholar.org/paper/284e538c9ffe8850c21dd2da89ec85ea916fa6da

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Citations in the corpus

Human mobility: Models and applications (2017, background): “Indeed, the elucidation and understanding of these patterns was motivated by its relation to several real-world applications such as traffic forecasting [3, 13, 14], urban planning [15, 10], internal security [16, 17] and epidemic modeling [18, 19, 20] to name but a few.”
Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal (2016, background): “From 1960 to 2011, urban share of Africa’s population rose from 19 percent to 39 percent and by 2040 half of Africa’s population will live in cities [2].”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, methodology): “[44] used phone data at the cell tower level to find that major traffic flows in congested roads in both the Boston and San Francisco Bay Areas are generated by very few driver sources.”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, methodology): “The method presented in this paper differs from existing studies that utilize CDR data to examine human travels in a trip-based approach [26], [38].”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, methodology): “Further, road usage inferred from the CDR data has been validated against GPS speed data (Wang et al., 2012) and highway assignment results from a travel demand model (Huntsinger and Donnelly, 2014).”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, methodology): “…methods have been proposed, for example, using a ratio of census-based population counts and observed cell phone users (Calabrese et al., 2011; Wang et al., 2012, 2014; Alexander et al., 2015), although this method can be biased when the ratio is high (e.g., over 20 for certain zones with low…”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “Moreover, they analyzed several aspects of mobility patterns ranging from human trajectories (Song et al., 2010) to migration (Simini et al., 2012) and road usage patterns (Wang et al., 2012).”
The path most traveled: Travel demand estimation using big data resources (2015, methodology): “Individual survey tracking and stay extraction (Asakura and Hato, 2004), OD-estimation and validation (Caceres et al., 2007; Nie et al., 2005; Wang et al., 2012; Iqbal et al., 2014), traffic speed estimation (Bar-Gera, 2007; Zhan et al.”