In this blog post, we look at 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. 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.
To study the distribution of travel demands over a day we divide it into four periods (Morning: 6 am–10 am, Noon & Afternoon: 10 am–4 pm, Evening: 4 pm–8 pm, Night: 8 pm–6 am) and cumulate trips over the total observational period. A trip is defined when the same mobile phone user is observed in two distinct zones within one hour (zones are defined by 892 towers’ service areas in the San Francisco Bay Area and by 750 census tracts in the Boston Area). In the mobile phone data, a user’s location information is lost when he/she does not use his/her phone, but by defining the transient origin and destination with movements within one hour, we can capture the distribution of travel demands. Specifically we calculate the t-OD as:
where A is the number of zones. W is the one-hour total trip production in the studied urban area, a number readily available for most cities. However this number gives no information about the trip distribution between zones, which we can enhance by the information gained via mobile phones. Directly from the mobile phone data we calculate Tij(n), which is the total number of trips that user nmade between zone i and zone j during the three weeks of study. Via calibrating Tij(n) for the total population we obtain: , where Nk is the number of users in zone k. The ratio M scales the trips generated by mobile phone users in each zone to the trips generated by the total population living there: M(k) = Npop(k)/Nuser(k), where Npop(k) and Nuser(k) are the population and the number of mobile phone users in zone k. Furthermore to assign only the fraction of the trips attributed to vehicles, we correct Fallij by the vehicle usage rate, which is a given constant for each zone and therefore obtain Fvehicleij .
For each mobile phone user that generated the t-OD, we can additionally locate the zone where he or she lives, which we define as the driver source. Connecting t-ODs with driver sources allows us for the first time to take advantage of mobile phone data sets in order to understand urban road usage. In the following, we present the analysis of the road usage characterization in the morning period as a case study.
A road network is defined by the links representing the road segments and the nodes representing the intersections. Using incremental traffic assignment, each trip in the t-OD matrix is assigned to the road network, providing us with estimated traffic flows (Fig.1(a)). The road network in the Bay Area serves a considerable larger number of vehicles per hour (0.73 million) than the one in the Boston (0.54 million). The traffic flow distribution P(V) in each area can be well approximated as the sum of two exponential functions corresponding to two different characteristic volumes of vehicles (Fig. 1a); one is the average traffic flow in their arterial roads (vA) and the other is the average traffic flow in their highways (vH). We measure (R2>0.99) with vA = 373 (236) vehicles/hour for arterials and vH = 1,493 (689) vehicles/hour for highways in the Bay Area (Boston numbers within parenthesis, pA and pH are the fraction of arterial roads and the fraction of highways). Both road networks have similar number of arterials (~20,000), but the Bay Area with more than double the number of highways than Boston (3,141 highways vs 1,267 in Boston) still receives the double of the average flow in the highways (vH) and a larger average flow in the arterial roads.