Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations Sajimon Abraham a,⇑ , P. Sojan Lal b a School of Management and Business Studies, Mahatma Gandhi University, Kottayam, Kerala, India b School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India article info Article history: Received 15 December 2010 Received in revised form 28 December 2011 Accepted 30 December 2011 Keywords: Spatio-temporal data mining Vehicle trajectory similarity Moving object databases Dimension reduction Sequence alignment Road traffic analysis abstract Data analysis based on the similarity of vehicle trajectories in a vehicular network is emerging as a new exciting paradigm that is important for law enforcement applications (e.g., the analysis of criminal tracking, road traffic security and traffic scheduling). Spatio-temporal data analysis plays an important role in many applications, including transportation infrastructure, border security and inland security. To analyze the moving patterns of vehicles on a road network, a measure for determining the similarity of vehicle trajectories with respect to space and time has to be defined. Although previous research has addressed the trajectory similarity problem, most of the studies focus on Euclidian dis- tance instead of network distance. This paper deals with the variations in applying a spatio- temporal similarity measure with given Points of Interest (POI) and Time of Interest (TOI), treating spatial similarity as a combination of structural and sequence similarities that is evaluated using the techniques of dynamic programming. The similarity set thus formed will be used by the remote database to broadcast trigger-based messages to participating vehicles in a neighborhood for future route- and information-sharing activities. The perfor- mance of the scheme is evaluated using experiments on standard real-life data. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction In recent years, information technology has significantly penetrated surface transportation. The transportation environ- ment is embedded with various mobile sensors, including on-board GPS receivers, sensors mounted on public transportation vehicles and pedestrian cell phones. These sensors continuously generate spatio-temporal data and enable applications such as vehicle tracking and environmental monitoring. Studying people and vehicle movements within a certain road network is both interesting and useful, especially if it can be used to understand, manage and predict the traffic flows. By studying the massive flow of traffic data as a trajectory, the traffic flow can be monitored and traffic-related patterns can be discovered. The development of Intelligent Transportation Systems (ITS) allows better monitoring and traffic control to optimize traffic flow. Advances in social networking and location-based services are increasingly creating new, sophisticated mechanisms that can foster a seamless integration of information among travelers to provide alternatives and support sustainable eco- nomic and social policies. Vehicular networks aimed at providing roadside services (e.g., traffic alerts, estimated time to reach a destination and alternative routes) improve the efficiency and safety on the road network. Unlike those in other networks, data management in vehicular networks is particularly challenging due to the following issues (Prashant, 2008; Xu et al., 2007): 0968-090X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.trc.2011.12.008 ⇑ Corresponding author. E-mail addresses: sajimabraham@redifmail.com (S. Abraham), sojanlal@gmail.com (P. Sojan Lal). Transportation Research Part C 23 (2012) 109–123 Contents lists available at SciVerse ScienceDirect Transportation Research Part C journal homepage: www.elsevier.com/locate/trc