IDENTIFICATION OF SIMILARITIES AND PREDICTION OF UNKNOWN FEATURES IN AN URBAN STREET NETWORK

<p><strong>Abstract.</strong> Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality.</p>

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Mining Topological Dependencies of Recurrent Congestion in Road Networks

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040248 ◽

2021 ◽

Vol 10 (4) ◽

pp. 248

Author(s):

Nicolas Tempelmeier ◽

Udo Feuerhake ◽

Oskar Wage ◽

Elena Demidova

Keyword(s):

Network Topology ◽

Real World ◽

Public Transportation ◽

Road Network ◽

Road Networks ◽

Traffic Load ◽

Data Mining Algorithm ◽

Urban Road ◽

The Road ◽

Spatio Temporal

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

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Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3488902 ◽

2022 ◽

Vol 13 (2) ◽

pp. 1-25

Author(s):

Bin Lu ◽

Xiaoying Gan ◽

Haiming Jin ◽

Luoyi Fu ◽

Xinbing Wang ◽

...

Keyword(s):

Traffic Flow ◽

Road Network ◽

Large Scale ◽

Transportation Systems ◽

Urban Traffic ◽

Propagation Mechanism ◽

Urban Road ◽

The Road ◽

Traffic Flow Forecasting ◽

Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

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Impact of Road Network Topology on Public Transportation Development

Wireless Communications and Mobile Computing ◽

10.1155/2021/6209592 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Minzhi Chen ◽

Fan Wu ◽

Min Yin ◽

Jiangang Xu

Keyword(s):

Public Transportation ◽

Road Network ◽

Road Networks ◽

Urban Road Network ◽

Urban Road ◽

The Road ◽

Layout Structure ◽

Per Capita ◽

Transportation Development ◽

The Impact

Planning of road networks is fundamental for public transportation. The impact of road network density on public transportation has been extensively studied, but few studies in this regard involved evaluation indicators for connectivity and layout of road networks. With 29 cities in China as the study cases, this paper quantifies the layout structure of the road network based on the network’s betweenness centralization and establishes a multivariate linear regression model to perform regression of the logarithm of the frequency of per capita public transportation on betweenness centralization. It is found in the present work that there is a significant correlation between the layout structure of an urban road network and the residents’ utilization degree of public transportation. A greater betweenness centralization of the urban road network, namely a more centralized road network, means a higher frequency of per capita public transportation of urban residents and a higher degree of the residents’ utilization of public transportation. In the development of public transportation, centralized and axial-shaped layout structures of road networks can be promoted to improve the utilization of public transportation.

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Model-based Identification, Estimation, and Control for Large-scale Urban Road Networks

2020 European Control Conference (ECC) ◽

10.23919/ecc51009.2020.9143995 ◽

2020 ◽

Author(s):

Isik Ilber Sirmatel ◽

Nikolas Geroliminis

Keyword(s):

Large Scale ◽

Road Networks ◽

Urban Road ◽

Model Based ◽

Estimation And Control ◽

And Control

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A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7120472 ◽

2018 ◽

Vol 7 (12) ◽

pp. 472 ◽

Cited By ~ 1

Author(s):

Bo Wan ◽

Lin Yang ◽

Shunping Zhou ◽

Run Wang ◽

Dezhi Wang ◽

...

Keyword(s):

Road Network ◽

Large Scale ◽

Graphics Processing Unit ◽

Road Networks ◽

Processing Unit ◽

Data Partition ◽

Matching Method ◽

The Road ◽

Central Processing ◽

Relaxation Matching

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

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Efficient Traffic State Estimation for Large-Scale Urban Road Networks

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2012.2218237 ◽

2013 ◽

Vol 14 (1) ◽

pp. 398-407 ◽

Cited By ~ 61

Author(s):

Qing-Jie Kong ◽

Qiankun Zhao ◽

Chao Wei ◽

Yuncai Liu

Keyword(s):

State Estimation ◽

Large Scale ◽

Road Networks ◽

Urban Road ◽

Traffic State ◽

Traffic State Estimation

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PART 1: Finance: Transport Strategy for Polish Cities

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1576-01 ◽

1997 ◽

Vol 1576 (1) ◽

pp. 1-9

Author(s):

Slobodan Mitric

Keyword(s):

Mass Transport ◽

Low Income ◽

Local Governments ◽

Large Scale ◽

Road Networks ◽

Cost Recovery ◽

Urban Transport ◽

Transport Infrastructure ◽

Rolling Stock ◽

Urban Road

A recent study requested by a group of mayors representing the largest Polish cities is summarized. The study was to be used as input into local and national debates about future directions of urban transport development in the country. The wider context is that of a major political and economic reform, begun in the late 1980s, involving no less than a rapidpaced transition from socialism to capitalism, featuring large-scale downsizing of the public sector, privatization, and a redistribution of political and resource powers from the state to local governments. Among the downstream effects of these changes has been an increase in private car ownership and use and a reduction in the market share of urban mass transit modes from between 80 and 90 percent of nonwalk daily trips to 70 percent or less. For transit operators, now owned by local governments, this has meant an added financial pressure coming after a decade of underinvestment in infrastructure, rolling stock, and other equipment. Large numbers of unemployed, retired, or otherwise low-income travelers, another consequence of restructuring the economy, have made it difficult to improve cost recovery by increasing fares. Traffic growth has generated congestion, since the structure and size of urban road networks were predicated on low car use. An urban transport strategy is proposed to respond to these problems. Its main short-term objective is to have an affordable and socially and environmentally acceptable modal split. In the longer term, the objective is to use the demand response to a much-reformed price system as the principal guide to how infrastructure and services should evolve. The key features of the strategy are as follows: ( a) evolution toward market-supplied services by a mixed-ownership mass transport industry; ( b) treatment of urban road networks as public utilities, focusing on cost recovery through pricing; ( c) linkage of pricing policies for mass transport and individual transport modes, in line with second-best thinking, aiming to reduce and even eliminate subsidies for both modes; and ( d) reliance on internally generated revenue leveraged by long-term borrowing to finance sectoral investments. It is therefore a counterpoint to a strategy wherein mass transport is a state-owned monopoly, the use of urban roads is subsidized as is mass transport, infrastructure investment is the instrument of preference as opposed to pricing, and sectoral investments and operating subsidies are financed from tax-generated budgets.

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