1 research outputs found
Modeling Traffic Networks Using Integrated Route and Link Data
Real-time navigation services, such as Google Maps and Waze, are widely used
in daily life. These services provide rich data resources in real-time traffic
conditions and travel time predictions; however, they have not been fully
applied in transportation modeling. This paper aims to use traffic data from
Google Maps and applying cutting-edge technologies in maximum likelihood
estimation to model traffic networks and travel time reliability. This paper
integrates Google Maps travel time data for routes and traffic condition data
for links to model the complexities of traffic networks. We then formulate the
Fisher information matrix and apply the asymptotic normality to obtain the
probability distribution of the travel time estimates for a random route within
the network of interest. We also derive the travel time reliability by
considering two levels of uncertainties, i.e., the uncertainty of the route's
travel time and the uncertainty of its travel time estimates. The proposed
method could provide a more realistic and precise travel time reliability
estimate. The methodology is applied to a small network in the downtown
Baltimore area, where we propose a link data collection strategy and provide
empirical evidence to show data independence by following this strategy. We
also show results for maximum likelihood estimates and travel time reliability
measures for different routes within the network. Furthermore, we use the
historical data from a different network to validate this approach, showing our
method provides a more accurate and precise estimate compared to the sample
mean of the empirical data.Comment: 11 pages, 4 figure