![]() ![]() To obtain better inherent consistency of dynamic routing behavior, substantial studies have used a time-series of traffic counts to develop time-dependent origin-destination (TDOD) estimation 32, 33, 34, 35, 36. 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. A recent study incorporated vehicle-driver behaviors into the macroscopic models 30, in which the behaviors were derived from microscopic traffic flows.įor macroscopic models, dynamic traffic assignment (DTA) can be used to estimate traffic flow patterns on the road network. Therefore, macroscopic models usually have fewer variables and need fewer properties 26, 27, which can simplify the computation of heat flux accumulation with higher reliability. traffic counting stations), and can overcome this problem because data are available for aggregation with steady and frequent updating. In contrast, macroscopic models usually utilize data collected from traffic sensors (e.g. A possible solution is through supersampling to extrapolate a system 29, which requires a complex maximum entropy model. ![]() 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. Hence, the models can estimate heterogeneous traffic flows appropriately since origin–destination (OD) matrices can be derived explicitly. GPS locations) to construct the trajectory of each vehicle, which is depicted as a time-series of vehicle locations 28. Microscopic models normally collect sporadic data with spatial information (e.g. 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. 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. The objective of this study is to develop a quantitative approach that can investigate the influence of vehicle movements on UHI. Thus, vehicular traffic should be considered as one of the major causes that increase the severity of UHI especially in mega cities such as Hong Kong. For example, the highest UHI intensity can be observed in the Kowloon peninsula of Hong Kong along major roads and road intersections, with a significant number of vehicles passing through every day 23. With regard to anthropogenic heat, vehicles can generate large amount of heat, and heat dispersion can be slowed down because of dense road networks and clusters of high-rise buildings 21, 22. They gradually reached a consensus that UHI is caused by (i) loss of greenery area over urbanization 11, 12, 13, 14 (ii) buildings blocking ventilation corridors and accumulating heat 15, 16 (iii) construction materials with low specific heat capacities absorbing solar radiations or reflecting solar radiations in densely built up areas 17 and (iv) increase of vehicles and growing electricity consumption producing more anthropogenic heat 18, 19, 20. In addition to estimating the magnitude of the UHI intensity, literature has been explicitly studied on their formation mechanism. UHI causes various adverse impacts to society in terms of health risk 3, 4, 5, public security 6, 7, 8, and energy consumption 9, 10. It is one of the major environmental issues caused by urbanization that generates more heat and adverse effects in local climate 1, 2, increasingly receiving public concern during the recent decades. ![]() Urban Heat Island (UHI) is an environmental phenomenon characterized by temperatures in urban areas being significantly higher than in surrounding rural areas. ![]()
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