Commuters’ traffic pattern and prediction analysis in a metropolitan area

Chavhan, Suresh; Venkataram, Pallapa

doi:10.1007/s41604-017-0004-z

Cited by 14 publications

(3 citation statements)

References 21 publications

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“…vs. non-rush hrs.) may also play a role in trend analysis and affect the correlation result [56]. However, to obtain more accurate measures, we would need thousands of images to select images that take at exactly the same time of the day throughout a period, at the moment, existing constellations do not offer this capacity yet.…”

Section: Discussionmentioning

confidence: 99%

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

et al. 2021

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The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations’ trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level.

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Section: Discussionmentioning

confidence: 99%

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

et al. 2021

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“…Bekasi City, a growing metropolitan city, faces several challenges in improving traffic management. These challenges include analysis and real-time traffic collection, traffic density, and travel time predictions on routes (Chavhan, S., & Venkataram, P., 2018). These challenges will impact the daily activities of society and the economy of commuters and public transport drivers.…”

Section: Traffic Managementmentioning

confidence: 99%

Traffic Management Effectivity of Bulak Kapal Underpass Development, Bekasi Indonesia

Rachmadina

Rifai

Handayani

2023

IJOMS

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Crossroads are one of the places that become a source of congestion. One of the intersections that are a source of congestion in Bekasi is the Bulak Kapal Intersection. This study aims to analyze the effectiveness of traffic management in the Bulak Kapal Underpass. The data collection method was carried out by distributing questionnaires offline and online. Data processing from respondents' questionnaire answers is presented using a spider chart with Microsoft Excel tools. The results showed that eleven parameter assessments before the Underpass was built and after the Underpass was built experienced an increase. The three parameters with the highest gap values or parameters that share a significant increase are traffic density, vehicle speed, and comfort. Meanwhile, the three parameters with the lowest gap values are road condition parameters, road completeness, and environmental friendliness.

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“…The continuous and rapid urban development witnessed in metropolitan areas has contributed to a surge in resource scarcity, heightened traffic congestion, and increased economic challenges (Chavhan and Venkataram 2020). In light of these pressing issues, quantitative analysis and accurate forecasts of commuting generation emerge as invaluable tools for optimizing transportation resource allocation, fortifying resilient city construction, and fostering interregional economic cooperation (Chavhan and Venkataram 2018;Ganin et al 2017;Martinus et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the commuting generation using metropolis-informed GCN and the topological commuter portrait

Chen,

Zhao,

Chen

2024

Transportation

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Understanding commuter traffic in transportation networks is crucial for sustainable urban planning with commuting generation forecasts operating as a pivotal stage in commuter traffic modeling. Overcoming challenges posed by the intricacy of commuting networks and the uncertainty of commuter behaviors, we propose MetroGCN, a metropolis-informed graph convolutional network designed for commuting forecasts in metropolitan areas. MetroGCN introduces dimensions of metropolitan indicators to comprehensively construct commuting networks with diverse socioeconomic features. This model also innovatively embeds topological commuter portraits in spatial interaction through a multi-graph representation approach capturing the semantic spatial correlations based on individual characteristics. By incorporating graph convolution and temporal convolution with a spatial–temporal attention module, MetroGCN adeptly handles high-dimensional dependencies in large commuting networks. Quantitative experiments on the Shenzhen metropolitan area datasets validate the superior performance of MetroGCN compared to state-of-the-art methods. Notably, the results highlight the significance of commuter age and income in forecasting commuting generations. Statistical significance analysis further underscores the importance of anthropic indicators for commuting production forecasts and environmental indicators for commuting attraction forecasts. This research contributes to technical advancement and valuable insights into the critical factors influencing commuting generation forecasts.

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Commuters’ traffic pattern and prediction analysis in a metropolitan area

Cited by 14 publications

References 21 publications

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

Spatial Temporal Analysis of Traffic Patterns during the COVID-19 Epidemic by Vehicle Detection Using Planet Remote-Sensing Satellite Images

Traffic Management Effectivity of Bulak Kapal Underpass Development, Bekasi Indonesia

Forecasting the commuting generation using metropolis-informed GCN and the topological commuter portrait

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