Taking the high road: Retrofitting amenity onto urban arterial roads

<p>Amenity values on urban arterial roads are fraught. This is largely due to the traditional traffic capacity versus amenity trade-off. This trade-off implies that high-capacity roads must be inherently deficient in amenity due to issues of air quality, noise nuisance and the physical barrier of streams of traffic. However, a more nuanced position – and one adopted by this thesis - is that arterial roads can be both busy thoroughfares and active, enjoyable destinations. This design-led research explores retrofitting amenity values onto existing arterial roads, creating new spaces and improving qualities of a system not originally constructed with amenity in mind. Cuba Street in Lower Hutt is a regionally significant connector intended for future densified development. In addition to the current link function, this road needs to become more attractive as a destination and address. Consideration at the urban scale encourages broad, strategic planning to support amenity holistically. This urban planning addresses topics like desirable densification, transit-oriented development, walkable centres and how these affect the arterial road condition. In moving from urban-scaled to architectural design, the detailed implementation of the greater policies is tested. Architecture is engaged to respond to the immediate arterial road conditions with spaces and surfaces, protective buffers and layers. In this way - with architectural refinement and a comprehensive, coherent strategy - traffic capacity and amenity can be brought into balance.</p>

Taking the high road: Retrofitting amenity onto urban arterial roads

<p>Amenity values on urban arterial roads are fraught. This is largely due to the traditional traffic capacity versus amenity trade-off. This trade-off implies that high-capacity roads must be inherently deficient in amenity due to issues of air quality, noise nuisance and the physical barrier of streams of traffic. However, a more nuanced position – and one adopted by this thesis - is that arterial roads can be both busy thoroughfares and active, enjoyable destinations. This design-led research explores retrofitting amenity values onto existing arterial roads, creating new spaces and improving qualities of a system not originally constructed with amenity in mind. Cuba Street in Lower Hutt is a regionally significant connector intended for future densified development. In addition to the current link function, this road needs to become more attractive as a destination and address. Consideration at the urban scale encourages broad, strategic planning to support amenity holistically. This urban planning addresses topics like desirable densification, transit-oriented development, walkable centres and how these affect the arterial road condition. In moving from urban-scaled to architectural design, the detailed implementation of the greater policies is tested. Architecture is engaged to respond to the immediate arterial road conditions with spaces and surfaces, protective buffers and layers. In this way - with architectural refinement and a comprehensive, coherent strategy - traffic capacity and amenity can be brought into balance.</p>

Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach

Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi’an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents.