Impacts of the COVID-19 pandemic on the spatio-temporal characteristics of a bicycle-sharing system: A case study of Pun Pun, Bangkok, Thailand

The COVID-19 pandemic is found to be one of the external stimuli that greatly affects mobility of people, leading to a shift of transportation modes towards private individual ones. To properly explain the change in people's transport behavior, especially in pre- and post- pandemic periods, a tensor-based framework is herein proposed and applied to Pun Pun-the only public bicycle-sharing system in Bangkok, Thailand-where multidimensional trip data of Pun Pun are decomposed into four different modes related to their spatial and temporal dimensions by a non-negative Tucker decomposition approach. According to our computational results, the first pandemic wave has a sizable influence not only on Pun Pun but also on other modes of transportation. Nonetheless, Pun Pun is relatively more resilient, as it recovers more quickly than other public transportation modes. In terms of trip patterns, we find that, prior to the pandemic, trips made during weekdays are dominated by business trips with two peak periods (morning and evening peaks), while those made during weekends are more related to leisure activities as they involve stations nearby a public park. However, after the first pandemic wave ends, the patterns of weekday trips have been drastically changed, as the number of business trips sharply drops, while that of educational trips connecting metro/subway stations with a major educational institute in the region significantly rises. These findings may be regarded as a reflection of the ever-changing transport behavior of people seeking a sustainable mode of private transport, with a more positive outlook on the use of bicycle-sharing system in Bangkok, Thailand

Particulate Matter Monitoring Using Inexpensive Sensors and Internet GIS: A Case Study in Nan, Thailand

Thailand has faced environmental issues that affect people all the time.  Haze from the forest fires for instance is concerned as national problem that we confront every year.  To determine the severity of smog conditions being a consequence of haze fire in some areas cannot be easily done.  Unmanned Aerial Vehicle (UAV) then could be easily used as a tool for surveying in such difficult burning areas.  Furthermore having the environmental sensing devices developed and mounted on the UAV would be a worthy approach for monitoring environmental status in hazardous areas. This research was conducted to assemble the UAV and sensor device for measuring the environmental data including temperature, humidity and dust particle.  The sensor was calibrated with reference devices.  The field test was carried out in Nan province. Together with temperature, humidity and dust particle value, the location and time from GPS on UAV will be integrated correspondingly with environmental measuring data.  Those entirely data will be imported to GIS and rendered in map form subsequently

Exploratory visualisation of congestion evolutions on urban transport networks

Visualisation is an effective tool for studying traffic congestion using massive traffic datasets collected from traffic sensors. Existing techniques can reveal where/when congested areas are formed, developed, and moved on one or several highway roads, but it is still challenging to visualise the evolution of traffic congestion on the whole road network, especially on dense urban networks. To address this challenge, this paper proposes three 3D exploratory visualisation techniques: the isosurface, the constrained isosurface, and the wall map. These three techniques have different advantages and should be combined to leverage their respective strong points. We present our visualisation techniques with the case of link travel time data from Automatic Number Plate Recognition (ANPR) in London