Abstract
Vehicular traffic has strong implication in the severity and degree of Urban Heat Island (UHI) effect in a city. It is crucial to map and monitor the spatio-temporal heat patterns from vehicular traffic in a city. Data observed from traffic counting stations are readily available for mapping the traffic-related heat across the stations. However, macroscopic models utilizing traffic counting data to estimate dynamic directional vehicular flows are rarely established. Our work proposes a simple and robust cell-transmission-model to simulate all the possible cell-based origin-destination trajectories of vehicular flows over time, based on the traffic counting stations. Result shows that the heat patterns have notable daily and weekly periodical circulation/pattern, and volumes of heat vary significantly in different grid cells. The findings suggest that vehicular flows in some places are the dominating influential factor that make the UHI phenomenon more remarkable.
Highlights
Urban Heat Island (UHI) is an environmental phenomenon characterized by temperatures in urban areas being significantly higher than in surrounding rural areas
Understanding the influence of vehicular flow on UHI requires an accurate estimation of the time-dependent traffic flows, i.e., the number of directional moving vehicles passing through a road network at a given time period
Traffic flow estimation in literature is mainly divided into two categories: microscopic traffic modeling which estimates the behavior of each individual vehicle[24,25] and macroscopic traffic modeling which describes the characteristics of traffic flows using aggregated parameters such as density and average speed[26,27]
Summary
Urban Heat Island (UHI) is an environmental phenomenon characterized by temperatures in urban areas being significantly higher than in surrounding rural areas. The models can estimate heterogeneous traffic flows appropriately since origin–destination (OD) matrices can be derived explicitly This approach is effective to reveal spatio-temporal traffic flow patterns but fails to provide reliable quantitative information of the vehicular traffic, since recording real-time location-based information of every vehicle is still a challenge. Dynamic traffic assignment (DTA) can be used to estimate traffic flow patterns on the road network. It is because DTA is formed by a principle of travel option which can determine (i) departure times, (ii) origins and destinations, (iii) travel routes of the vehicles, and (iv) a traffic flow module, that it can trigger the propagation of traffic flows over time[31]. To simplify the model and to adapt this model to a big-data computational module at the best convenience, this study will still use a LWR model to investigate a series of parameters for refining the results
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