Constant increase in urban road traffic is forcing traffic authorities to confront unavoidable congestion problems these days. This creates new challenges for city planners to generate traffic routes with better designs and improve the existing roads. In order to analyze and improve traffic routes, there is a need to understand movement patterns of major vehicular traffic inside the city. Origin Destination(OD) estimation is one such method for understanding the movement patterns. The objective is to find an optimal OD matrix for city traffic, which is a subtle process for classic algorithms. The solution to this problem is Genetic Algorithm (GA) approach which is a search heuristics to find optimal solution from a set of random solutions. A Genetic Algorithm approach has been developed to find an optimal OD matrix, showing the actual travel patterns of significant number of vehicles inside the city. Once the best OD matrix is obtained, it yields a big opportunity for traffic planners to analyze and improve traffic scenario. Genetic Algorithm can play a major role in solving complex problems. In this paper, one such problem of estimating OD matrix of a city is discussed and implemented. In conjunction to genetic algorithm, Mean Absolute Percentage Error(MAPE) has been used as a fitness criteria for finding the optimal OD matrix.
Distributed computing is the method of running CPU intensive computations on multiple computers collectively in order to achieve a common objective. Common problems that can be solved on the distributed systems include climate/weather modeling, earthquake simulation, evolutionary computing problems and so on. These type of problems may involve billions or even trillions of computations. A single computer is not capable to finish these computations in short span of time, which is typically in days. Distributed computation helps to solve these problems in hours, which could take weeks to solve on a single computer. Distributed computing generally uses the existing resources of the organization.Traffic simulation is the process of simulating transportation systems through software on a virtual road network. Traffic simulation helps in analyzing city traffic at different time intervals of a single day. Common use cases could be analyzing city wide traffic, estimating traffic demand at a particular traffic junction and so on. This paper discusses about the approach to use distributed computing paradigm for optimizing the traffic simulations. Optimizing simulations involves running a number of traffic simulations followed by estimating the nearness of that simulation to the real available traffic data. This real data could be obtained by either manual counting at traffic junctions, or using the probes such as loop inductors, CCTV cameras etc. This distributed computing based approach works to find the best traffic simulation corresponding to the real data in hand, using evolutionary computing technique.
Origin Destination has become a crucial aspect in long term transportation planning. For Origindestination estimations, wide variety of methods can be used. Conventional methods like home surveys & roadside monitoring are slow & less effective. Bluetooth & CCTV cameras are also feasible methods for doing OD study, but have their own downsides. At present, this information contributes to very less percentage of data collection. Ubiquitous technologies like mobile phones being deployed in the proposed research is estimated to enhance the data collection and provide a quick & effective OD estimation. In this paper we discuss how technology becomes the future vehicle for OD.
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