<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars."
Abstract:"The ever-increasing number of automobiles, especially private cars, has irrevocably altered social life, revolutionized travel experience, and reshaped the face of urban transportation systems. Existing research mainly focuses on trajectory data from floating cars, while ha man mobility and travel behavior by private cars are still little understood. To bridge this gap, we employ a private car trajectory data set collected from real-world urban environments. This article provides a new perspective on human mobility, with a focus on private cars, by investigating mobility patterns and capturing the spatiotemporal evolution of urban hot zones from people's arrive-stay-leave (ASL) behavior. Understanding these hot zones and travel behavior patterns is necessary when considering changes or improvements in urban networks. Decisions impacting the safety and emissions impacts of urban travel, including, for example, design improvements at curves or to reduce left-turn conflicts, improvements to pedestrian facilities, and the placement of alternative energy refueling stations, will rely on similar analyses across many different urban areas. Our article provides a proof of concept, and we then outline new research opportunities and challenges involving private car trajectory data Along with the increased process of urbanization and in-dustriallzation, recent years have witnessed an ever-increasing number of automobiles, especially private cars [1], also known as passenger cars, those with less than seven seats and usually registered to individuals for personal use. Compared to floating cars [2], private cars constitute the vast majority of automobiles. For instance, the nationwide ownership of automobiles in China exceeded 263 million by the end of 2019 [3], and more than 88% of these vehicles (225 million) were registered to individuals. As shown in Figure 1(a), ownership of private cars exhibited a growing trend from 2013 to 2019. As seen in Sivak's technical report [4], per capita car ownership in the United States was 0.756 in 2015 and increased to 0.766 by 2016. According to [5], in the last two decades, private cars accounted for more than 75% of passenger transport activity in the European Union. As shown in Figure 1(b), even though a slightly decreasing trend is expected by 2030, it is still expected to be larger than 72%.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
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Q3 = [2];
Q4 = [0];
Q5 = [];
Q6 = [1].
Exploring Human Mobility Patterns and Travel Behavior: A Focus on Private Cars
The ever-increasing number of automobiles, especially private cars, has irrevocably altered social life, revolutionized travel experience, and reshaped the face of urban transportation systems. Existing research mainly focuses on trajectory data from floating cars, while ha man mobility and travel behavior by private cars are still little understood. To bridge this gap, we employ a private car trajectory data set collected from real-world urban environments. This article provides a new perspective on human mobility, with a focus on private cars, by investigating mobility patterns and capturing the spatiotemporal evolution of urban hot zones from people's arrive-stay-leave (ASL) behavior. Understanding these hot zones and travel behavior patterns is necessary when considering changes or improvements in urban networks. Decisions impacting the safety and emissions impacts of urban travel, including, for example, design improvements at curves or to reduce left-turn conflicts, improvements to pedestrian facilities, and the placement of alternative energy refueling stations, will rely on similar analyses across many different urban areas. Our article provides a proof of concept, and we then outline new research opportunities and challenges involving private car trajectory data Along with the increased process of urbanization and in-dustriallzation, recent years have witnessed an ever-increasing number of automobiles, especially private cars [1], also known as passenger cars, those with less than seven seats and usually registered to individuals for personal use. Compared to floating cars [2], private cars constitute the vast majority of automobiles. For instance, the nationwide ownership of automobiles in China exceeded 263 million by the end of 2019 [3], and more than 88% of these vehicles (225 million) were registered to individuals. As shown in Figure 1(a), ownership of private cars exhibited a growing trend from 2013 to 2019. As seen in Sivak's technical report [4], per capita car ownership in the United States was 0.756 in 2015 and increased to 0.766 by 2016. According to [5], in the last two decades, private cars accounted for more than 75% of passenger transport activity in the European Union. As shown in Figure 1(b), even though a slightly decreasing trend is expected by 2030, it is still expected to be larger than 72%.