Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator
DOI: 10.1016/j.trd.2007.05.003
archive: archived pipeline: cataloged verified
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Summary
This paper introduces the Dynamic ROad traffic NoisE (DRONE) simulator, a tool designed to estimate road traffic noise in urban networks by integrating traffic simulation with noise estimation models. The development of DRONE addresses limitations in existing noise estimation models, which typically fail to account for time-dependent traffic demand and complex acoustic interactions in built-up areas, such as multiple diffractions and reflections from buildings. By linking dynamic traffic flow data with spatial noise calculations, DRONE generates areawide, time-dependent noise contour maps, facilitating the evaluation of noise abatement policies and strategic urban planning. The methodology combines the AVENUE mesoscopic traffic simulation model, which provides dynamic traffic flow characteristics with one-second resolution, with the Acoustic Society of Japan (ASJ) Model-1998 for noise estimation. The ASJ model calculates equivalent continuous A-weighted sound pressure levels ($L_{Aeq}$) based on vehicle type, speed, and distance. To address built-up areas, the model employs the Uesaka statistical approach, which estimates building attenuation by dividing the area into front and rear groups of buildings and calculating sound energy propagation through and over them. The system divides the study area into a grid of receptor points, calculating noise contributions from all source sections on the road network, and links the output to a Geographical Information System (GIS) for visualization. Validation against measured data showed a maximum difference of less than 1 dB(A) in non-built-up areas and approximately 4 dB(A) in built-up areas, both within acceptable limits. The implementation of DRONE was demonstrated using the Ikegami Shinmachi intersection in Tokyo, analyzing a 1 km² core area and a larger 5 km by 3 km whole area. The study evaluated various noise abatement policies, including bans on heavy vehicles, lower speed limits, low-noise pavement, and sound walls. Results indicated that traffic management measures, such as banning heavy vehicles, reduced noise in the core area but caused a diversion of traffic that increased noise levels in the broader whole area. In contrast, infrastructure investments like sound walls significantly reduced noise across both areas. For instance, removing sound walls increased the percentage of affected residential buildings in the core area from 16% to 83%. The study concluded that while traffic management policies have low installation costs, they may shift noise problems geographically, whereas infrastructure measures provide more consistent noise reduction but require higher capital investment.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-19 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | failed | — | — | — | 4 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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