Mahmood Rahmani

 

Travel Time Distribution Estimation/Prediction Using Floating Car Data 

 

Abstract:

The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. Most travellers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Currently, most systems collecting floating car data (FCD) are designed to transmit the data in a limited form and relatively infrequently due to the cost of data transmission. That means the reported locations of vehicles are far apart in time and space. In order to extract traffic information from the data, it first needs to be matched to the underlying digital road network. Matching such sparse data to the network, especially in dense urban, area is challenging.
The aim of this project is to estimate/predict the status of road traffic in terms of travel time distribution using FCD. Some applications requiring travel time information are traffic monitoring and management, journey planning, fleet management, etc. To begin with FCD, A map-matching and path inference algorithm for sparse GPS probes is developed. The method is utilised in a case study in Stockholm and showed robustness and high accuracy compared to a number of other methods in the literature.
A non-parametric travel time estimation method is developed and used to process FCD from 1500 taxis in Stockholm City. The taxi data had been ignored because of its low frequency and minimal information. The proposed method showed that such sparse data can be processed and transformed into information (e.g. travel time distribution) that is suitable for traffic studies.
The project is also contributed to the implementation of an experimental ITS laboratory, called iMobility Lab since 2010. The lab is designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control (visit www.imobilitylab.se for more information).
Currently, The focus is on developing a travel time prediction method using historical and real-time FCD.

 

Advisor:  Professor Haris N. Koutsopoulos.

University: Royal Institute of Technology KTH).

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