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Real-time navigation services, such as Google Maps and Waze, are widely usedin daily life. These services provide rich data resources in real-time trafficconditions and travel time predictions; however, they have not been fullyapplied in transportation modeling. This paper aims to use traffic data fromGoogle Maps and applying cutting-edge technologies in maximum likelihoodestimation to model traffic networks and travel time reliability. This paperintegrates Google Maps travel time data for routes and traffic condition datafor links to model the complexities of traffic networks. We then formulate theFisher information matrix and apply the asymptotic normality to obtain theprobability distribution of the travel time estimates for a random route withinthe network of interest. We also derive the travel time reliability byconsidering two levels of uncertainties, i.e., the uncertainty of the route'stravel time and the uncertainty of its travel time estimates. The proposedmethod could provide a more realistic and precise travel time reliabilityestimate. The methodology is applied to a small network in the downtownBaltimore area, where we propose a link data collection strategy and provideempirical evidence to show data independence by following this strategy. Wealso show results for maximum likelihood estimates and travel time reliabilitymeasures for different routes within the network. Furthermore, we use thehistorical data from a different network to validate this approach, showing ourmethod provides a more accurate and precise estimate compared to the samplemean of the empirical data.

Modeling Traffic Networks Using Integrated Route and Link Data