Approximating Shortest Path in Large-Scale Road Networks with Turn Prohibitions Using Multi-constrained Path Algorithm

2013 ◽  
Author(s):  
Krao Girish ◽  
Sandeep Sarkar ◽  
S. Rajagopalan
Keyword(s):  
Shortest Path ◽  
Large Scale ◽  
Road Networks

The shortest path problem on large-scale real-road networks

Networks ◽  
2006 ◽  
Vol 48 (4) ◽  
pp. 182-194 ◽  
Author(s):  
G.A. Klunder ◽  
H.N. Post
Keyword(s):  
Shortest Path ◽  
Large Scale ◽  
Road Networks ◽  
Shortest Path Problem

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

2018 ◽  
Vol 7 (12) ◽  
pp. 472 ◽  
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.

Approximate path searching for supporting shortest path queries on road networks

2015 ◽  
Vol 325 ◽  
pp. 409-428 ◽  
Author(s):  
Chun Jiang Zhu ◽  
Kam-Yiu Lam ◽  
Song Han
Keyword(s):  
Shortest Path ◽  
Road Networks ◽  
Path Queries

PART 1: Finance: Transport Strategy for Polish Cities

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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