MAPS IN HISTORY: Annihilation of space by (travel) time

Distance Matters: a more than euclidean approach to visualizing gerrymandering

Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days

In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.

<p indent="0mm">During the Mongol-Yuan dynasty, many tourists travelled along the Silk Road, of whom about 15 have detailed records which could be used to restore their journeys. The journeys of those tourists are of great significance to the study of varied routes of the Silk Road in different periods of the Mongol-Yuan dynasty. This study first collected the travel points of the tourists from literature, and then drew their travel routes based on modern research findings, historical and modern maps, Google Earth satellite images, and so on. The travel of the 15 tourists, from the earliest Yelü Chucai (1218) to the latest Marignolli (1353), had a time span of more than a century. According to the travel time of each tourist, we categorized the total 673 locations into 20 groups of travel points and the total 64 sections of travel routes into 20 groups, thus forming this data set.