Socioeconomic and Cultural Importance of The Yellow-Fronted Canary (Serinus Mozambicus) in Northern Benin

The species yellow-fronted canary undergoes a real traffic due to its song and medico-magical properties in order to supply local commercial networks. This study aims to assess the socioeconomic and cultural importance of this bird in order to identify the cohorts of actors involved along this activity chain. In this context a survey was carried out among 400 breeders in four agroecological zones of Benin (ZAE II; ZAE III, ZAE IV and ZAE V). The questionnaire also focused on general characteristics of the respondents on aspects related to general knowledge of the bird, its social importance, purchase and sale price, cultural importance and breeders’ perceptions. Canary traders, mostly with primary education level (63.25%) and Muslim (97.5%), were on average 44 years old and an average experience of 27 years. They exercised this activity in part-time (96.5%). The knowledge about canary is shared is different depending on the ethnic group. The activity was profitable for all of them (100%) with 128,624 FCFA (233.18 USD) net profit per month. The trader's education level, whether or not he belonged to ZAE II, the number of birds he had in his possession, the daily amount he invested in their food, their selling price, the number of years he made the activity, his experience in the business were the factors that determine the net profit of the canary’s trader. However, canaries' breeding in north-Benin is based on a complex system with actors who develop many myths around medico-magical practices. This study was necessary to better assess the pressure on the species in order to anticipate its conservation.

Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data

Data-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable development, etc. However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. Existing approaches for modeling and identifying human activities based on massive social sensing data rely on a large number of valid supervised samples or limited prior knowledge. This paper proposes an effective methodology for building human activity chains based on mobile phone signaling data and labeling activity purpose semantics to analyze human activity patterns, spatiotemporal behavior, and urban dynamics. We fully verified the effectiveness and accuracy of the proposed method in human daily activity process construction and activity purpose identification through accuracy comparison and spatial-temporal distribution exploration. This study further confirms the possibility of using big data to observe urban human spatiotemporal behavior.

Spatial Distribution of Travel Activities and its Relationship with Points of Interest

This study explores the spatial distribution characteristics of travel activities and their relationship with land use, using data from the resident travel survey in 2015 of Xiaoshan District of Hangzhou City, China. A new classification method is proposed to classify the travel activity patterns into three groups: single-activity travel, multi-activity intermittent travel, and multi-activity continuous travel. The main findings are: (a) the length of activity chain and the proportion of multi-activity travels increase with the distance between residence and activity centre; (b) the non-home destinations of single-activity travel, multi-activity intermittent travel and multi-activity continuous travel agglomerate towards the activity centre, and the degree of agglomeration increases in this order; (c) the distribution density of Point Of Interest (POI) and activity destinations have strong positive correlations in space; (d) some attributes of POIs and demographics have significant influence on multi-activity continuous travels. These findings are useful in inducing the activities through reasonable combinations and spatial interconnections of POIs in urban planning.

Numerical simulation tools for PVT collectors and systems

The computer-based experimentation covers almost the entire activity chain of the PVT sector. The PVT community carries out very different kind of modelling and simulation labours in order to answer to very diverse needs, such as proof-of-concepts, research, design, sizing, controlling, optimization, validation, marketing, sales, O&M, etc.

Fusing Mobile Phone and Travel Survey Data to Model Urban Activity Dynamics

A key issue to understand urban system is to characterize the activity dynamics in a city—when, where, what, and how activities happen in a city. To better understand the urban activity dynamics, city-wide and multiday activity participation sequence data, namely, activity chain as well as suitable spatiotemporal models, are needed. The commonly used household travel survey data in activity analysis suffers from limited sample size and temporal coverage. The emergence of large-scale spatiotemporal data in urban areas, such as mobile phone data, provides a new opportunity to infer urban activities and the underlying dynamics. However, the challenge is the absence of labeled activity information in mobile phone data. Consequently, how to fuse the useful information in household survey data and mobile phone data to build city-wide, multiday, and all-time activity chains becomes an important research question. Moreover, the multidimension structure of the activity data (e.g., location, start time, duration, type) makes the extraction of spatiotemporal activity patterns another difficult problem. In this study, the authors first introduce an activity chain inference model based on tensor decomposition to infer the missing activity labels in large-scale and multiday activity data, and then develop a spatiotemporal event clustering model based on DBSCAN, called STE-DBSCAN, to identify the spatiotemporal activity patterns. The proposed approaches achieved good accuracy and produced patterns with a high level of interpretability.

Simulation of the Daily Activity Plans of Travelers Using the Park-and-Ride System and Autonomous Vehicles: Work and Shopping Trip Purposes

The preferences of travelers determines the utility of daily activity plans. Decision-makers can affect the preference of travelers when they force private car users to use park-and-ride (P&R) facilities as a way of decreasing traffic in city centers. The P&R system has been shown to be effective in reducing uninterrupted increases in traffic congestion, especially in city centers. Therefore, the impacts of P&R on travel behavior and the daily activity plans of both worker and shopper travelers were studied in this paper. Moreover, autonomous vehicles (AVs) are a promising technology for the coming decade. A simulation of the AV as part of a multimodal system, when the P&R system was integrated in the daily activity plans, was carried out to determine the required AV fleet size needed to fulfill a certain demand and to study the impacts of AVs on the behavior of travelers (trip time and distance). Specifically, a group of travelers, who use private cars as their transport mode, was studied, and certain modifications to their daily activity plans, including P&R facilities and changing their transport mode, were introduced. Using the MATSim open-source tool, four scenarios were simulated based on the mentioned modifications. The four scenarios included (1) a simulation of the existing transport modes of the travelers, (2) a simulation of their daily activity plans when their transport modes were changed to AVs, (3) a simulation of the travelers, when P&R facilities were included in their activity chain plans, and (4) a simulation of their daily activity plans, when both P&R and AVs were included in their activity chain plans. The result showed that using the P&R system increased overall travel time, compared with using a private car. The results also demonstrated that using AVs as a replacement for conventional cars reduced travel time. In conclusion, the impact of P&R and AVs on the travel behavior of certain travelers was evaluated in this paper.