Optimized CapsNet for Traffic Jam Speed Prediction Using Mobile Sensor Data under Urban Swarming Transportation

Tampubolon, Hendrik; Yang, Chao-Lung; Chan, Arnold Samuel; Sutrisno, Hendri; Hua, Kai-Lung

doi:10.3390/s19235277

Cited by 15 publications

(7 citation statements)

References 30 publications

(38 reference statements)

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“…The size of Dataset #1 is 600 Gigabytes with more than two billion traffic speed records. Table 2 shows the example of the processed Dataset #1, and more detailed information of the preprocessing data can be found in the previous work [ 3 ]. Four main attributes in Dataset #1 were used to build Jakarta’s traffic speed: the time information, location latitude, location longitude, and the recorded speeds.…”

Section: Resultsmentioning

confidence: 99%

“…Weather is an essential causing factor of traffic congestion, especially in metropolises of developing countries. Due to the limited infrastructure, such as flawed mass transport, deficient sewer systems, and relatively narrow roads, in developing countries, such as Vietnam or Indonesia [ 1 , 2 , 3 ], traffic is vulnerable when it rains. Meanwhile, the astronomical economic loss caused by the congestion highlights the urgency of the solutions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Yang

Sutrisno

Chan

et al. 2021

Sensors

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Weather change such as raining is a crucial factor to cause traffic congestion, especially in metropolises with the limited sewer system infrastructures. Identifying the roads which are sensitive to weather changes, defined as weather-sensitive roads (WSR), can facilitate the infrastructure development. In the literature, little research focused on studying weather factors of developing countries that might have deficient infrastructures. In this research, to fill the gap, the real-world data associating with Jakarta, Indonesia, was studied to identify WSR based on smartphone sensor data, real-time weather information, and road characteristics datasets. A spatial-temporal congestion speed matrix (STC) was proposed to illustrate traffic speed changes over time. Under the proposed STC, a sequential clustering and classification framework was applied to identify the WSR in terms of traffic speed. In this work, the causes of WSR were evaluated based on the variables’ importance of the classification method. The experimental results show that the proposed method can cluster the roads according to the pattern changes in the traffic speed caused by weather change. Based on the results, we found that the distances to shopping malls, mosques, schools, and the roads’ altitude, length, width, and the number of lanes are highly correlated to WSR in Jakarta.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Yang

Sutrisno

Chan

et al. 2021

Sensors

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“…Moreover, some studies have evaluated the impact of different policies, such as the use of geographic information systems in planning the expansion of the system [113] and the design of parking spaces exclusively for motorcycles in the city center [114], on urban transport systems. Other isolated works have included two-wheelers in the prediction of road congestion using sensors [89] and are linked to the sustainability cluster through the smart city category.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, the terms climate change and smart city are also new in the field (2019). The first one assesses the impacts of modes of transport on climate change and human health [88], and the second one proposes a method to predict congestion on roads using sensors [89].…”

Section: Most Common Topicsmentioning

confidence: 99%

Mapping the Research Landscape for the Motorcycle Market Policies: Sustainability as a Trend—A Systematic Literature Review

Cadavid

Salazar-Serna

2021

Sustainability

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The motorcycle market has experienced an upward trend. That growth brings along mobility, accidents, and environment-related issues; nevertheless, there is a scarcity of literature on evaluating the impact of motorcycle market policies. Consequently, it has been challenging for researchers and policymakers to develop evidence-based strategies to promote or control the growth of this market. This paper aims to review and analyze the scientific literature about motorcycle market policies, using tech-mining techniques and a cluster analysis of keywords, to provide insights about the most relevant world trends in this research area. For this purpose, the bibliographic information of publications in the field was retrieved from the Scopus database. As a result, three thematic clusters (sustainability, mobility, and electric motorcycles) were identified and explained. According to our findings, greenhouse gas emissions, sustainability, environmental impact, and developing countries are the hot research topics. The research leader countries on said topics are the United States, Germany, and the United Kingdom. This study can, therefore, be used as a reference to define a future research agenda in the area. Consequently, it permits researchers and policymakers to identify trending topics and gaps in knowledge, as a baseline to include motorcycles in sustainable and affordable transport systems design.

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“…Therefore, research on traffic data has gradually become a research hotspot. H. Tampubolon et al used mobile sensor data for traffic jam speed prediction [6]. B. Ji et al used LTE data to predict road traffic [7].…”

Section: Traffic Data Analysismentioning

confidence: 99%

Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

Zhang

Lin

et al. 2020

Applied Sciences

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More and more people in mega cities are choosing to travel by public transportation due to its convenience and punctuality. It is widely acknowledged that there may be some potential associations between passengers. Their travel behavior may be working together, shopping together, or even some abnormal behaviors, such as stealing or begging. Thus, analyzing association between passengers is very important for management departments. It is very helpful to make operational plans, provide better services to passengers and ensure public transport safety. In order to quickly explore the association between passengers, we propose a multi-view interactive exploration method that provides five interactive views: passenger 3D travel trajectory view, passenger travel time pixel matrix view, passenger origin-destination chord view, passenger travel vehicle bubble chart view and passenger 2D travel trajectory view. It can explore the associated passengers from multiple aspects such as travel trajectory, travel area, travel time, and vehicles used for travel. Using Beijing public transportation data, the experimental results verified that our method can effectively explore the association between passengers and deduce the relationship.

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Optimized CapsNet for Traffic Jam Speed Prediction Using Mobile Sensor Data under Urban Swarming Transportation

Cited by 15 publications

References 30 publications

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

Mapping the Research Landscape for the Motorcycle Market Policies: Sustainability as a Trend—A Systematic Literature Review

Multi-View Interactive Visual Exploration of Individual Association for Public Transportation Passengers

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