Data-driven analysis of weather impacts on urban traffic conditions at the city level

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

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Measurements of urban traffic parameters before and after road reconstruction

Open Engineering ◽

10.1515/eng-2021-0035 ◽

2021 ◽

Vol 11 (1) ◽

pp. 365-376

Author(s):

Andrzej Bąkowski ◽

Leszek Radziszewski

Keyword(s):

Urban Traffic ◽

Noise Model ◽

Vehicle Traffic ◽

The Road ◽

Passenger Vehicles ◽

Before And After ◽

Traffic Volumes ◽

National Road ◽

The City ◽

Good Agreement

Abstract The study analyzed the parameters of vehicle traffic and noise on the national road in the section in the city from 2011 to 2016. In 2013–2014 this road was reconstructed. It was found that in most cases, the distribution of the tested variable was not normal. The median and selected percentiles of vehicle traffic parameters and noise were examined. The variability and type A uncertainty of the results were described and evaluated. The results obtained for the data recorded on working and non-working days were compared. The vehicle cumulative speed distributions, for two-way four-lane road segments in both directions were analyzed. A mathematical model of normalized traffic flow has been proposed. Fit factor R2 of the proposed equations to the experimental data for passenger vehicles ranges from 0.93 to 0.99. It has been shown that two years after the road reconstruction, the median noise level did not increase even though traffic volumes and vehicle speeds increased. The Cnossos noise model was validated for data recorded over a period of 6 years. A very good agreement of the medians determined according to the Cnossos-EU model and the measured ones was obtained. It should be noted, however, that for the other analyzed percentiles, e.g. 95%, the discrepancies are larger.

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TripRes

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3401979 ◽

2021 ◽

Vol 17 (2) ◽

pp. 1-21

Author(s):

Xiaolong Xu ◽

Zijie Fang ◽

Lianyong Qi ◽

Xuyun Zhang ◽

Qiang He ◽

...

Keyword(s):

Traffic Flow ◽

Discrete Distribution ◽

Resource Provisioning ◽

Path Selection ◽

Urban Traffic ◽

Traffic Flows ◽

Multimedia Services ◽

Traffic Flow Prediction ◽

The City ◽

Multimedia Surveillance

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

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Noise prediction for urban traffic conditions—related to measurements in the Sydney Metropolitan Area

Applied Acoustics ◽

10.1016/0003-682x(77)90002-0 ◽

1977 ◽

Vol 10 (1) ◽

pp. 1-7 ◽

Cited By ~ 37

Author(s):

M.A. Burgess

Keyword(s):

Metropolitan Area ◽

Urban Traffic ◽

Noise Prediction ◽

Traffic Conditions

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Data-Driven Scenario Specification for AV–VRU Interactions at Urban Roundabouts

Sustainability ◽

10.3390/su13158281 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8281

Author(s):

Andreas Keler ◽

Patrick Malcolm ◽

Georgios Grigoropoulos ◽

Seyed Abdollah Hosseini ◽

Heather Kaths ◽

...

Keyword(s):

Autonomous Vehicle ◽

Urban Traffic ◽

Data Driven ◽

Design Elements ◽

Flow Simulations ◽

Complex Interactions ◽

3D Environments ◽

Fictional Space ◽

Motorized Vehicles ◽

Simulator Test

Detailed specifications of urban traffic from different perspectives and scales are crucial for understanding and predicting traffic situations from the view of an autonomous vehicle (AV). We suggest a data-driven specification scheme for maneuvers at different design elements of the built infrastructure and focus on urban roundabouts in Germany. Based on real observations, we define classes of maneuvers, interactions and driving strategies for cyclists, pedestrians and motorized vehicles and define a matrix for merging different maneuvers, resulting in more complex interactions. The sequences of these interactions, which partially consist of explicit communications, are extracted from real observations and adapted into microscopic traffic flow simulations. The simulated maneuver sequences are then visualized in 3D environments and experienced by bicycle simulator test subjects. Using trajectory segments (in fictional space) from two conducted simulator studies, we relate the recorded movement patterns of test subjects with observed cyclists in reality.

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Strangers in the City: Transmigration from Eastern Europe and its Impact on Berlin and Hamburg 1880–1914

Journal of Migration History ◽

10.1163/23519924-00202002 ◽

2016 ◽

Vol 2 (2) ◽

pp. 223-246

Author(s):

Tobias Brinkmann

Keyword(s):

United States ◽

New York ◽

Eastern Europe ◽

The United States ◽

Urban Traffic ◽

European Cities ◽

Massive Growth ◽

Transit Migration ◽

The Impact ◽

The City

This article examines the impact of transit migration from the Russian and Austro-Hungarian Empires on Berlin and Hamburg between 1880 and 1914. Both cities experienced massive growth during the last three decades of the nineteenth century, and both served as major points of passage for Eastern Europeans travelling to (and returning from) the United States. The rising migration from Eastern Europe through Central and Western European cities after 1880 coincided with the need to find adequate solutions to accommodate a rapidly growing number of commuters. The article demonstrates that the isolation of transmigrants in Berlin, Hamburg (and New York) during the 1890s was only partly related to containing contagious disease and ‘undesirable’ migrants. Isolating transmigrants was also a pragmatic response to the increasing pressure on the urban traffic infrastructure.

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Factors of the safety in bicycle traffic in the City of Zagreb and its surrounding

Glasnik srpskog geografskog drustva ◽

10.2298/gsgd1403055s ◽

2014 ◽

Vol 94 (3) ◽

pp. 55-68

Author(s):

Josko Sindik

Keyword(s):

Weather Conditions ◽

Urban Traffic ◽

Questionnaire Study ◽

Local Authorities ◽

Driving Style ◽

Study Participants ◽

Risk Driving ◽

The City

The aim of this study was to determine the differences in underlying factors of Zagreb cycling, compared to the "types of cyclists" (driving style), i.e. different ways of using bicycles as a means of transport. The study included over 3,000 frequent participants in urban traffic cycling, sample of members of the association Cyclist Union (N = 1259) and snowball sample of "typical" of cyclists, i.e. people who are using the bike, but are not the members of the Cyclist Union (N = 1831), using the conveniently assembled questionnaire. Study participants who bike used in various applications prefer the safest driving style (only on sidewalks and bike paths / lines). Barriers of the weather conditions are ubiquitous in the safest driving style. Daily, weekly and yearly riding a bicycle are more often found in those who prefer the safest driving style. Cyclists who drive with medium secure style (roads with less traffic and lower speeds), more often ride a bike, as compared with those who prefer the safest driving style. Having a better bike line / track and other infrastructure is the most often considered at those with the highest risk driving style. The results provide the guidance for local authorities and for the cyclists to improve the conditions for a safer and more often by bicycle circulation in the City of Zagreb and its surroundings.

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Research on Decision Evaluation Model of HOV Lane Setting

Scientific Programming ◽

10.1155/2021/1688824 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Ding Lv ◽

Qunqi Wu ◽

Bo Chen ◽

Yahong Jiang

Keyword(s):

Traffic Congestion ◽

Road Traffic ◽

Evaluation Model ◽

Path Selection ◽

Urban Traffic ◽

Urban Rail Transit ◽

Rail Transit ◽

Travel Efficiency ◽

Hov Lanes ◽

The City

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

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