<|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:"Estimation and monitoring of city-to-city travel times using call detail records."
Abstract:"Whenever someone makes or receives a call on a mobile telephone, a Call Detail Record (CDR) is automatically generated by the operator for billing purposes. CDRs have a wide range of applications beyond billing, from social science to data-driven development. Recently, CDRs have been increasingly used to study human mobility, whose understanding is crucial e.g. for planning efficient transportation infrastructure. A major difficulty in analyzing human mobility using CDR data is that the location of a cell phone user is not recorded continuously but typically only when a call is initiated or a text message is sent. In this paper we address this problem, and develop a method for estimating travel times between cities based on CDRs that relies not on individual trajectories of people, but their collective statistical properties. We apply our method to data from Senegal, released by Sonatel and Orange for the 2014 Data for Development Challenge. We turn CDR mobility traces to estimates on travel times between Senegalese cities, filling an existing gap in knowledge. Moreover, the proposed method is shown to be highly valuable for monitoring travel conditions and their changes in near real-time, as demonstrated by measuring the decrease in travel times due to the opening of the Dakar-Diamniadio highway. Overall, our results indicate that it is possible to extract reliable de facto information on typical travel times that is useful for a variety of audiences ranging from casual travelers to transport infrastructure planners.."
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:
Q1 = [1];
Q2 = [];
Q3 = [2];
Q4 = [0];
Q5 = [];
Q6 = [1].
Estimation and monitoring of city-to-city travel times using call detail records
Whenever someone makes or receives a call on a mobile telephone, a Call Detail Record (CDR) is automatically generated by the operator for billing purposes. CDRs have a wide range of applications beyond billing, from social science to data-driven development. Recently, CDRs have been increasingly used to study human mobility, whose understanding is crucial e.g. for planning efficient transportation infrastructure. A major difficulty in analyzing human mobility using CDR data is that the location of a cell phone user is not recorded continuously but typically only when a call is initiated or a text message is sent. In this paper we address this problem, and develop a method for estimating travel times between cities based on CDRs that relies not on individual trajectories of people, but their collective statistical properties. We apply our method to data from Senegal, released by Sonatel and Orange for the 2014 Data for Development Challenge. We turn CDR mobility traces to estimates on travel times between Senegalese cities, filling an existing gap in knowledge. Moreover, the proposed method is shown to be highly valuable for monitoring travel conditions and their changes in near real-time, as demonstrated by measuring the decrease in travel times due to the opening of the Dakar-Diamniadio highway. Overall, our results indicate that it is possible to extract reliable de facto information on typical travel times that is useful for a variety of audiences ranging from casual travelers to transport infrastructure planners.