Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions
Збережено в:
| Опубліковано в:: | PLoS One vol. 10, no. 5 (May 2015), p. e0127095 |
|---|---|
| Автор: | |
| Опубліковано: |
Public Library of Science
|
| Предмети: | |
| Онлайн доступ: | Citation/Abstract Full Text Full Text - PDF |
| Теги: |
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 1683369407 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0127095 |2 doi | |
| 035 | |a 1683369407 | ||
| 045 | 2 | |b d20150501 |b d20150531 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Manley, Ed | |
| 245 | 1 | |a Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions | |
| 260 | |b Public Library of Science |c May 2015 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC) framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain. | |
| 653 | |a Economic | ||
| 653 | |a Global positioning systems--GPS | ||
| 653 | |a Data processing | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Markov chains | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Navigation behavior | ||
| 653 | |a Random walk | ||
| 653 | |a Traffic flow | ||
| 653 | |a Traffic models | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Computer simulation | ||
| 653 | |a Markov analysis | ||
| 653 | |a Urban areas | ||
| 653 | |a Traffic | ||
| 653 | |a Roads & highways | ||
| 773 | 0 | |t PLoS One |g vol. 10, no. 5 (May 2015), p. e0127095 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1683369407/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1683369407/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1683369407/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |