Identification of spillovers in urban street networks based on upstream fixed traffic data

2014 ◽  
Vol 18 (5) ◽  
pp. 1539-1547 ◽  
Author(s):  
Dongfang Ma ◽  
Dianhai Wang ◽  
Yiming Bie ◽  
Sheng Jin ◽  
Zhenyu Mei
Keyword(s):  
Traffic Data ◽  
Urban Street ◽  
Street Networks

The Development of Pompeii’s Urban Street Network

2017 ◽  
Author(s):  
Eric E. Poehler
Keyword(s):  
Urban Form ◽  
Formal Analysis ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
The City ◽  
Present Understanding

Chapter 2 explores the present understanding of Pompeii’s evolution by disassembling the apparent patchwork of grids across the city and reconsiders the presumed awkwardness in their adhesion. To do this, the traditional tools of formal analysis—street alignments and block shapes—are employed with and critiqued by the stratigraphic evidence recovered in the last three decades of excavation below the 79 CE levels. The result is an outline of the development of Pompeii’s urban form as a series of street networks: from the archaic age, through the period of the “hiatus” of the fifth and fourth centuries BCE, to a reorganization of the city’s space so profound that it can genuinely be considered a refoundation, and finally to the adjustments of a refounded city in the Colonial, Augustan, and post-earthquake(s) periods.

Prompt prediction of spillovers in urban street networks by using rolling time occupancy data

2019 ◽  
Vol 15 (2) ◽  
pp. 1041-1060
Author(s):  
Dongfang Ma ◽  
Bowen Sheng ◽  
Dianhai Wang ◽  
Sheng Jin ◽  
Xiang Song
Keyword(s):  
Urban Street ◽  
Street Networks

An exploratory evaluation of urban street networks for last mile distribution

Cities ◽  
2020 ◽  
Vol 107 ◽  
pp. 102916
Author(s):  
Julia Coutinho Amaral ◽  
Claudio B. Cunha
Keyword(s):  
Urban Street ◽  
Street Networks ◽  
Last Mile

Resilience of Urban Street Network Configurations under Low Demands

2020 ◽  
Vol 2674 (9) ◽  
pp. 982-994
Author(s):  
Zhengyao Yu ◽  
Vikash V. Gayah
Keyword(s):  
Routing Algorithm ◽  
Shortest Paths ◽  
Left Turn ◽  
Urban Street ◽  
Traffic Conditions ◽  
Street Networks ◽  
Shortest Distance ◽  
Left Turns ◽  
The Impact ◽  
Travel Distances

Urban street networks are subject to a variety of random disruptions. The impact of movement restrictions (e.g., one-way or left-turn restrictions) on the ability of a network to overcome these disruptions—that is, its resilience—has not been thoroughly studied. To address this gap, this paper investigates the resilience of one-way and two-way square grid street networks with and without left turns under light traffic conditions. Networks are studied using a simplified routing algorithm that can be examined analytically and a microsimulation that describes detailed vehicle dynamics. In the simplified method, routing choices are enumerated for all possible origin–destination (OD) combinations to identify how the removal of a link affects operations, both when knowledge of the disruption is and is not available at the vehicle’s origin. Disruptions on two-way networks that allow left turns tend to have little impact on travel distances because of the availability of multiple shortest paths between OD pairs and the flexibility in route modification. Two-way networks that restrict left turns at intersections only have a single shortest-distance path between any OD pair and thus experience larger increases in travel distance, even when the disruption is known ahead of time. One-way networks sometimes have multiple shortest-distance routes and thus travel distances increase less than two-way network without left turns when links are disrupted. These results reveal a clear tradeoff between improved efficiency and reduced resilience for networks that have movement restrictions, and can be used as a basis to study network resilience under more congested scenarios and in more realistic network structures.

scholarly journals Urban Street Networks, a Comparative Analysis of Ten European Cities

2013 ◽  
Vol 40 (6) ◽  
pp. 1071-1086 ◽  
Author(s):  
Emanuele Strano ◽  
Matheus Viana ◽  
Luciano da Fontoura Costa ◽  
Alessio Cardillo ◽  
Sergio Porta ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Urban Street ◽  
Street Networks ◽  
European Cities

Dynamic Speed Harmonization in Connected Urban Street Networks

2018 ◽  
Vol 33 (6) ◽  
pp. 510-523 ◽  
Author(s):  
Mehrdad Tajalli ◽  
Ali Hajbabaie
Keyword(s):  
Urban Street ◽  
Street Networks ◽  
Speed Harmonization

scholarly journals Multifractal scaling analyses of urban street network structure: The cases of twelve megacities in China

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246925
Author(s):  
Yuqing Long ◽  
Yanguang Chen
Keyword(s):  
Fractal Dimension ◽  
Clustering Algorithm ◽  
Scaling Law ◽  
Central Area ◽  
Direct Determination ◽  
Traffic Networks ◽  
Street Network ◽  
Urban Street ◽  
Street Networks ◽  
Multifractal Scaling

Traffic networks have been proved to be fractal systems. However, previous studies mainly focused on monofractal networks, while complex systems are of multifractal structure. This paper is devoted to exploring the general regularities of multifractal scaling processes in the street network of 12 Chinese cities. The city clustering algorithm is employed to identify urban boundaries for defining comparable study areas; box-counting method and the direct determination method are utilized to extract spatial data; the least squares calculation is employed to estimate the global and local multifractal parameters. The results showed multifractal structure of urban street networks. The global multifractal dimension spectrums are inverse S-shaped curves, while the local singularity spectrums are asymmetric unimodal curves. If the moment order q approaches negative infinity, the generalized correlation dimension will seriously exceed the embedding space dimension 2, and the local fractal dimension curve displays an abnormal decrease for most cities. The scaling relation of local fractal dimension gradually breaks if the q value is too high, but the different levels of the network always keep the scaling reflecting singularity exponent. The main conclusions are as follows. First, urban street networks follow multifractal scaling law, and scaling precedes local fractal structure. Second, the patterns of traffic networks take on characteristics of spatial concentration, but they also show the implied trend of spatial deconcentration. Third, the development space of central area and network intensive areas is limited, while the fringe zone and network sparse areas show the phenomenon of disordered evolution. This work may be revealing for understanding and further research on complex spatial networks by using multifractal theory.

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