Aiming for better mobility and more efficient utilization of transportation networks, emergent connected and autonomous vehicle (CAV) technologies, and the resulting communication capabilities can produce more coordinated and efficient routing behavior. Current routing strategies either rely on a centralized control system which can fail in scaling, or employ decentralized schemes that yield sub-optimal coordination and accordingly poor system performance. This paper presents a Decentralized Collaborative Time-dependent Shortest Path Algorithm (Dec-CTDSP) with which the CAVs optimize their routes according to the communicated mobility messages from the other CAVs within their connected cluster. These messages are assumed to carry information regarding the vehicles’ location, speed, and preferred path to their destination. We analyzed the impacts of this optimization scheme under various levels of CAV market penetration and communication radius. The results of this study reveal (1) Up to 40% improvement in mean system travel time and speed; (2) Up to 45% increase in travel time and speed prediction reliability; (3) A strong correlation between mean system travel time and network usage distribution; and (4) significant improvements in network utilization as a result of Dec-CTDSP. The performance of Dec-CTDSP, in terms of runtime, convergence, and mobility improvements, is further benchmarked against other random and Dijkstra-based algorithms. The findings of this work will help steer further research on the implementation of coordinated and decentralized multiagent routing optimization in the context of connected and autonomous vehicles.

Although geo-tagged mobility data (e.g., cell phone data and social media data) can be potentially used to estimate individual space-time travel trajectories, they often have low sample rates that only tell travelers' whereabouts at the sparse sample times while leaving the remaining activities to be estimated with interpolation. This study proposes a set of time geography-based measures to quantify the accuracy of the trajectory estimation in a robust manner. A series of measures including activity bandwidth and normalized activity bandwidth are proposed to quantify the possible absolute and relative error ranges between the estimated and the ground truth trajectories that cannot be observed. These measures can be used to evaluate the suitability of the estimated individual trajectories from sparsely sampled geo-tagged mobility data for travel mobility analysis. We suggest cutoff values of these measures to separate useful data with low estimation errors and noisy data with high estimation errors. We conduct theoretical analysis to show that these error measures decrease with sample rates and people's activity ranges. We also propose a lookup table-based interpolation method to expedite the computational time. The proposed measures have been applied to 2013 geo-tagged tweet data in New York City and 2014 cell-phone data in Shenzhen, China. The results illustrate that the proposed measures can provide estimation error ranges for exceptionally large datasets in much shorter times than the benchmark method without using lookup tables. These results also reveal managerial results into the quality of these data for human mobility studies, including their distribution patterns.