Understanding Road Usage Patterns in Urban Areas

Abstract

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.I n an era of unprecedented global urbanization, society faces a rapidly accelerating demand for mobility, placing immense pressure on urban road networks 1,2 . This demand manifests in the form of severe traffic congestion 3,4 , which decreases the roads' level of service, while at the same time increasing both fuel consumption 5 and traffic-related air pollution 6 . In 2007 alone, congestion forced Americans living in urban areas to travel 4.2 billion hours more, purchase an additional 2.8 billion gallons of fuel, at a total cost of $87.2 billion 3 . To mitigate congestion in urban roads, urban planning 1 , traffic prediction 7-9 and the study of complex networks [10][11][12][13][14][15] have been widely investigated potential influencing factors. However, without comprehensive knowledge of how roads are used dynamically, these studies are conventionally based on expensive and quickly outdated travel surveys or segmented information on traffic flow and travel time 7-9 , which fail to support the researchers with the information needed to cope with modern mobility demand. Up to now our understanding of the origins of the drivers in each road remained limited and not quantitatively solved.In this work we validate for the first time a methodology, which employs comprehensive mobile phone data to detect patterns of road usage and the origins of the drivers. Thus, providing a basis for better informed transportation planning, including targeted strategies to mitigate congestion 3,4 . We formalize the problem by counting the observed number of individuals moving from one location to another, which we put forward as the transient origin destination (t-OD) matrix (Fig. S5, Fig. S11 and Supplementary Information (SI) section II.A). Traditionally, ODs are costly and difficult to obtain, because they are at best based on travel diaries made every few years, which quickly become obsolete and strongly rely on provided reports 7,8 . In contrast, the rapidly increasing penetration rates and massive usage of mobile phones, with towers densely located in urban areas, can provide the most detailed information on daily human mobility [16][17][...

A review of urban computing for mobile phone traces

“…The coordinates of the records are estimated by a standard triangulation algorithm (and the data do not include cell phone tower information). The accuracy of the location is about 200-to 300-meters, which is of higher resolution than representing locations by cell towers [44,42,5]. This finer granularity enables us to identify locations of users more accurately and thus to adapt data preprocessing methods that have been previously applied to GPS records [48,47,49,17].…”

Discovering urban activity patterns in cell phone data

“…(Gonzalez et al 2008;Schneider et al 2013;Ratti et al 2006;Ratti et al 2007;Sevtsuk and Ratti 2010;Calabrese et al 2013;Hoteit et al 2014), origin-destination flows, e.g. (Tettamanti and Varga 2014;Wang et al 2013;Caceres et al 2012;Calabrese et al 2011;Friedrich et al 2010), and road usage patterns (Wang et al 2012). More detailed reviews of the use of cell phone data in traveler information systems and travel behavior studies can be found in (Qiu and Cheng 2007;Yue et al 2014;Wang et al 2014).…”

Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach

“…Dong et al [37] used CDRs to suggest traffic zone division in urban areas to assist travel demand forecast. Wang et al [38] used mobile phone data collected in San Francisco and Boston area to evaluate urban road usage patterns. It is clear that mobile phone data can be leveraged to uncover human travel demand associated with different transportation modes and activity types in various urban contexts.…”