An efficient MapReduce-based parallel clustering algorithm for distributed traffic subarea division

Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs). Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K -Means (Par3PKM) algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K -Means and then employ a MapReduce paradigm to redesign the optimized K -Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K -Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data

Modeling And Optimizing Route Choice For Multimodal Transportation Networks

Traffic congestion has been one of the major issues that most urban areas are facing and thus, many solutions have been developed and deployed in order to mitigate its negative effects. Advanced Traveler Information Systems (ATIS) have been used over the past two decades to provide travelers with pre-trip or real-time traffic information. Most of the efforts have focused on providing timely traffic information at locations with regularly occurring congestion. ATIS can be used to provide travelers with pre-trip and on-route travel information necessary to improve trip decision making with respect to various criteria (e.g. minimizing delay, constraining travel to specific modes). Many jurisdictions within Canada and the United States have implemented the 511 travel information system that provides traffic information, road conditions and closures, traffic cameras, etc. Several studies were conducted on vehicle routing optimization methods in ATIS. Most of them consider passenger vehicles as the only transportation mode in their routing algorithm. Others that include two transportation modes are mostly based on shortest path algorithms. However, a probabilistic based route optimization approach could better capture the stochastic characteristic of road traffic conditions. This research investigates an adaptive routing methodology for multi-modal transportation networks. A routing algorithm based on Markov decision processes is proposed to capture short-term traffic characteristics of transportation networks. Graph theory is used to model typical travel behavior within a multimodal network. This thesis proposes to use special network modeling elements, e.g. super nodes, to allow the integration of public transportation schedule into the model via the publicly available predefined timetables. The proposed routing algorithm applies an iterative function to select the optimal transportation mode/route through the network junctions along a given path. The proposed methodology is applied to several real-world networks of motorized and non-motorized modes located in the central business district in Toronto, Ontario, and Montreal and Longueuil in Quebec. The networks include train, bus, streetcar, subway and bicycle transportation facilities. Microsimulation models of the networks developed in VISSIM and AIMSUN are used to estimate travel times along major arterials, for all transportation modes and for different traffic demands and congestion levels. The simulation models were calibrated using volume and speed data. The developed routing algorithm is applied to several scenarios in order to estimate optimal routes for a hypothetical traveler moving between two arbitrarily selected nodes in the network. The results identify the most efficient combination of transportation modes that the travelers have to use given specific constraints pertaining to traffic and transit service conditions. It is also shown that by applying the proposed algorithm to bus lines, transit agencies can have significant cost savings by rerouting their fleet. The results of the proposed research have the potential to be integrated into various Intelligent Transportation Systems applications by combining available traveler information services. It can assist travelers in making more informed decisions regarding their travel plans and provide transportation agencies with an overall assessment of the system and its performance. For example, it can be used to minimize the impact of congested traffic conditions on the overall travel time and/or cost incurred by travelers as well as the operating cost of transit agencies