Mapping the worst commute with cellular network data: A case of Shanghai

摘要

Peak-hour vehicular traffic jams, associated productivity loss, fuel consumption, air pollution, and Greenhouse Gas (GHG) emissions are now epidemics across many populous cities in the world. In the US alone, for instance, traffic jams result in US$ 121 billion in total delay and fuel costs (RITA, 2013). Arguably, commuting trips, especially those by solo-drivers, contribute substantially to the epidemics. Some typical reactions of policy-analysts and planners to deal with traffic jams are: (1) To increase the carrying capacity of the transportation system. (2) To reduce the number of commuting trips or at least to shorten those trips. (3) To efficiently distribute commuting trips across modes of travel, space and time.