Explore the diverse representations of highway networks through distinct graph structures, each tailored with unique edge connections and node features. Dive into the dynamics of traffic flow across various time frames within a unified structural framework.
Nodes
Explore the intricacies of highway network analytics by integrating sensor node data and constructing feature vectors to capture the state and dynamics of sensors within specific time frames:
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Speed Window: Captures dynamic speed variations by utilizing a window of previous aggregated speeds from specific time intervals.
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Lane Count: Identifies the constant number of lanes adjacent to each sensor as a critical factor for understanding traffic flow dynamics and bottleneck phenomena.
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Day of Week: Helps in understanding how traffic patterns vary throughout the week, considering factors like weekday rush hours and weekend leisure travel.
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Hour of Day: Incorporates the time of day, using a sinusoidal and cosine transformation to capture the cyclical nature of traffic patterns, notably during rush hours.
Edges
Dive into the spatial dynamics of highway networks through a comprehensive understanding of edge connections:
Type 1: Sequential Linkage
- Linear Representation: Establish direct connections between each sensor and its immediate neighbors along the same highway.
- Straightforward Connectivity: This type of edge ensures a sequential linkage, portraying the immediate spatial relationships within the network.
Type 2: External Influences
- Capturing External Dynamics: Acknowledge potential influences from sensors external to a given highway, accounting for traffic diversions and alternate routes.
- Nuanced Representation: These edges provide insights into traffic patterns influenced by external factors, enhancing the understanding of inter-highway dynamics.
Type 3: Long-Range Dependencies
- Capturing Extended Interactions: Grasp the impact of traffic dynamics originating from distant locales within a designated highway.
- Efficient Representation: Utilize skip connections to capture long-range interactions directly, mitigating the challenges posed by extensive propagation through intermediate sensors.
