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

Chao-Lung Yang,Hendri Sutrisno,Budhi Sholeh Wibowo,Hendrik Tampubolon,Arnold Samuel Chan

doi:10.3390/s21072405

Abstract

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.

Highlights

Weather is an essential causing factor of traffic congestion, especially in metropolises of developing countries
This paper considers three datasets for studying weather-sensitive roads (WSR): Dataset #1 is a traffic speed dataset based on smartphone sensors data, Dataset #2 is a weather dataset, and Dataset #3 contains roads’ characteristics
This study focuses on identifying and analyzing the causes of WSR using machine learning methods based on smartphone sensors, weather, and road characteristics datasets

Summary

Introduction

Weather is an essential causing factor of traffic congestion, especially in metropolises of developing countries. The astronomical economic loss caused by the congestion highlights the urgency of the solutions. The tremendous economic loss underlines the importance of solving traffic congestion in the metropolises of developing countries. Researchers have proposed alternative and shortterm solutions to remind drivers about traffic congestion, such as understanding the traffic patterns on different weather conditions [7,8,9]. In intelligent transport system (ITS) research, the traffic prediction models try to predict future traffic congestion in terms of location and time. In Taipei City, Taiwan, many traffic bulletin boards were installed on major roads to announce real-time information regarding the roads’ traffic situation and prediction under different weather conditions. Other ITS applications include adaptive street lighting control [11 the traffic flow using camera devices installed on Strinella Street in L’Aquila c this research [11], the traffic prediction model was used to provide i2noffo1r8mation fic congestion and provide a more efficient energy consumption of traffic lighti

Methods

Results

Conclusion