Abstract
In highly populated urban zones, it is common to notice headway deviations (HD) between pairs of buses. When these events occur in a bus stop, they often cause bus bunching (BB) in the following bus stops. Several proposals have been suggested to mitigate this problem. In this paper, we propose to find BBS (Bunching Black Spots) - sequences of bus stops where systematic HD events cause the formation of BB. We run a sequence mining algorithm, named PrefixSpan, to find interesting events available in time series. We prove that we can accurately model the BB trip usual pattern like a frequent sequence mining problem. The subsequences proved to be a promising way of identify the route' schedule points to adjust in order to mitigate such events. © 2012 Springer-Verlag.

Abstract
Vehicular sensing is emerging as a powerful mean to collect information using the variety of sensors that equip modern vehicles. These sensors range from simple speedometers to complex video capturing systems capable of performing image recognition. The advent of connected vehicles makes such information accessible nearly in real-time and creates a sensing network with a massive reach, amplified by the inherent mobility of vehicles. In this paper we discuss several applications that rely on vehicular sensing, using sensors such as the GPS receiver, windshield cameras, or specific sensors in special vehicles, such as a taximeter in taxi cabs. We further discuss connectivity issues related to the mobility and limited wireless range of an infrastructure-less network based only on vehicular nodes. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.