CITIES USING TRANSCAD DRIVERS
CITIES USING TRANSCAD DRIVER

(c) The number of major dynamical driver sources N MDDS of a road segment is well approximated by the exponential distribution ( R 2 > 0.99) in the Bay area and the exponential distribution in the Boston area ( R 2 > 0.99). (b) Traffic flow in the Boston area road network. (a) Traffic flow in the Bay area road network. Sources of traffic congestion in the Bay area and Boston area. 1cвЂ“f) were based on the census tracts for both Bay area and Boston area. All measurements regarding the dynamical driver sources ( Fig. The defined zones were only used in the process of generating the t-ODs. The different zone definitions were resulted from the different features of location records in the two mobile phone datasets (see Methods). In this study, zones were defined by towers' servicing areas in the Bay area and census tracts in the Boston area. S2a, see Methods for definition of displacement) and counted the number of trips F ij between each pair of zones i and j.

First, we defined a trip as a displacement occurring within one hour in a specific time period ( Fig. to 4:00 p.m.) and the evening period (4:00вЂ“8:00 p.m.) were estimated ( Fig. To capture the dynamical urban travel demand, transient ODs ( t-ODs) for the morning period (6:00вЂ“10:00 a.m.), the noon/afternoon period (10:00 a.m.

CITIES USING TRANSCAD DRIVERS

Here, based on large-scale road GIS data and mobile phone data in the San Francisco Bay area and the Boston area (see Methods), a method was developed to target the road clusters most heavily used by drivers from time-variant sources of traffic congestion, thus providing a basis for better informed urban traffic control, which includes the mitigation of traffic congestion. However, a modeling framework was still missing, which would encapsulate travel demand information into urban roads and dynamically adapt road networks to the time-variant and rapidly growing mobility demands in this era of unprecedented global urbanization. These investigations, whether employing new statistical measures developed in complex network theory 22, 23, or uncovering abundant information hidden in large-scale data, tremendously revolutionized the tools for understanding urban rhythms 24 and solving urban problems 25. Additionally, laws and models of human mobility were studied using large-scale mobility data to estimate urban travel demand 17, 18, 19, 20, 21. Connectedness 1, spatial accessibility 7, betweenness centrality 8, 9, the minimum spanning tree 10, the price of anarchy 11 and optimal navigation strategies 12, 13, 14 were all investigated to understand road network topology and improve transport efficiency 15, 16. Given the importance of urban road networks, their robustness and efficiency have drawn widespread attention in various scientific and engineering fields. For example, building new roads can increase the road network supply, whereas reducing the usage of private vehicles is capable of decreasing demand of road usage. Faced with rapidly accelerating travel demands, transportation agencies had tried to alleviate traffic congestion through various methods, which can be classified into two groups depending upon whether they increase road supply or decrease travel demand 3, 4, 5, 6. While each city has its own constraints in geography, history and socio-economic mechanisms, road networks from very diverse cities were all developed under the co-evolutions of urban travel demands and road network supplies 2. Urban road network, one of the crucial infrastructures in cities, facilitates people's daily commutes and maintains modern society's ability to function properly 1, 2.

Cities using transcad