Johannes Fürle
Biographic Data
| ID | 7074799 |
|---|---|
| NAME | Johannes Fürle |
| GIVEN NAMES | Johannes |
| FAMILY NAME | Fürle |
| SIGNATURE | FÜRLE J |
| AFFILIATIONS | Heidelberg University |
| ORCID | 0000-0002-3306-3732 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
How to assess the needs of vulnerable population groups towards heat-sensitive routing
Heat poses a significant risk to human health, particularly for vulnerable populations, such as pregnant women, older individuals, young children and people with pre-existing medical conditions. In view of this, we formulated a heat stress-avoidant routing approach in Heidelberg, Germany, to ensure mobility and support day-to-day activities in urban areas during heat events. Although the primary focus is on pedestrians, it is also applicable to c…
Private Vehicles Greenhouse Gas Emission Estimation at Street Level for Berlin Based on Open Data
As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches …
SocialMedia2Traffic
Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Unfortunately, open data are rarely available for this purpose. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic c…
No prominent works on this page.
SocialMedia2Traffic
Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Unfortunately, open data are rarely available for this purpose. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic c…
Private Vehicles Greenhouse Gas Emission Estimation at Street Level for Berlin Based on Open Data
As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches …
How to assess the needs of vulnerable population groups towards heat-sensitive routing
Heat poses a significant risk to human health, particularly for vulnerable populations, such as pregnant women, older individuals, young children and people with pre-existing medical conditions. In view of this, we formulated a heat stress-avoidant routing approach in Heidelberg, Germany, to ensure mobility and support day-to-day activities in urban areas during heat events. Although the primary focus is on pedestrians, it is also applicable to c…
Computer Science (2 works) · Environmental Science (2 works) · Traffic Prediction and Management Techniques (2 works) · Transport engineering (2 works) · Artificial Intelligence (1 work) · Betweenness centrality (1 work) · Business (1 work) · Centrality (1 work) · COVID-19 impact on air quality (1 work) · Data Management and Algorithms (1 work)