Automated taxi dispatching system with accurate travel time prediction

Abstract

Fast and reliable transportation systems demonstrate the wealth of a country. Aim of this project is to develop an integrated system which is capable of addressing both identify patterns of traffic to estimate the travel time for specific route and real-rime update of current incidents on the route. Estimate the travel time for specific destination, identify the shortest time consuming path among optional paths and provide the real time update for route incidents are the main objectives of this automated system. The solution is to develop an automated taxi dispatching system without using operators to communicate with passengers and also providing the travel time to user destination. Travel time prediction with high accuracy is the major task of this system. So the key role of this approach is the predicted speed value of the identified route. This report illustrates the importance of having clear details of longitude, latitude data and other traffic related data for accurate decision making of taxi dispatching. In this prediction process mainly used artificial neural networks to train the database. Pattern recognition leads to identify patterns of traffic related data. This paper presents a novel approach which builds a travel time prediction model based on a historical traffic data and other speed varying factors. Evaluation results shown that the acceptable accuracy of travel speed prediction and how to affect speed varying factors to the error values. This concept will be fruitful for many taxi dispatching systems and fleet management as well as traffic related travel time prediction systems.