چكيده به لاتين
Abstract:
Travel time is a particularly important input for assessing and monitoring the efficiency and performance of a transport network and its associated traffic management systems. Ranging from city planning to individual travelers, they all make decisions based on average travel time or variability of travel time among other factors.
The use of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place, with significant number of mobile sensors moving around covering expansive areas of the road network. Many travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles in commercial fleets are now routinely equipped with GPS. Currently, systems that collect probe vehicle data are designed to transmit data in a limited form and relatively infrequently due to the cost of data transmission. Thus, reported locations are far apart in time and space. Estimation of travel time with such data is challenging.
This dissertation introducing first purely GIS – based travel time estimation algorithm for sparse historical probe vehicle data. The algorithm presented in this study utilizes mainly built-in functions in the GIS for map matching and path inference. Then, Distanse – Speed – based method developed in the dissertation is used for estimation of travel time. Taxi GPS data from three urban routes at morning peak period over four days (for each route) were collected using mobile phones and were used to implementation and evaluation of proposed method. The results of the "map matching and path inference" step showed that the proposed method could correctly identify 91.69% of the links travelled by vehicles. The estimated links travel times validated against real travel time data obtained from frequently GPS data (1 second resolution) and results display a "mean absolute percentage error (MAPE)"of 6.86%. Comparison of the estimated travel time for all routes (from origin to destination) with the real travel time of those routes showed that about 92% of the routes travel times were estimated with an error of less than 14.5% and MAPE = 6.63%. Also, by comparing the mean estimated route travel time with the mean travel time obtained from the license plate matching method, the MAPE value is equal to 7.15%
Keywords: Historical GPS data, Sparse data, Map matching, Path inference, Travel time estimation, GIS.
