Multistage Impacts of the Heavy Rain Process on the Travel Speeds of Urban Roads

Abstract

Heavy rain causes the highest drop in travel speeds compared with light and moderate rain because it can easily induce flooding on road surfaces, which can continue to hinder urban transportation even after the rainfall is over. However, very few studies have specialized in researching the multistage impacts of the heavy rain process on urban roads, and the cumulative effects of heavy rain in road networks are often overlooked. In this study, the heavy rain process is divided into three consecutive stages, i.e., prepeak, peak, and postpeak. The impact of heavy rain on a road is represented by a three-dimensional traffic speed change ratio vector. Then, the k-means clustering method is implemented to reveal the distinct patterns of speed change ratio vectors. Finally, the characteristics of the links in each cluster are analyzed. An empirical study of Shenzhen, China suggests that there are three major impact patterns in links. The differences among links associated with the three impact patterns are related to the road category, travel speeds in no rain days, and the number of transportation facilities. The findings in this research can contribute to a more in-depth understanding of the relationship between the heavy rain process and the travel speeds of urban roads and provide valuable information for traffic management and personal travel in heavy rain weather.

A Systematic Literature Review of the Hybrid Methodologies in Assessing Flood Indirect Impacts on Transportation

“…The vast majority of articles modeling Asia are concentrated on China [17]. This could be influenced by the fact that China has a history of summer flooding, but recent deforestation, reclaiming of wetlands, and storage of water for power generation and irrigation have made it more vulnerable [18].…”

“…Overall, researchers have primarily investigated the indirect effects of flooding on transportation in major metropolitan areas of developed countries with well-established infrastructures. Some examples of these areas include York [19,21], New York [37], Shanghai [38], Shenzhen [18], and Seoul [39]. On the other hand, there has been limited research publications in the literature for municipalities with inadequate or low-quality infrastructure, particularly in developing countries.…”

“…Dynamic integration takes into account the temporal propagation of floods, whereas static integration assumes a stable flood condition throughout the modeling period. The static integration of the flood and traffic models produces just a binary representation of flooding, which can be a reasonable solution for assessing the effects of the flood on the transport system when the flood propagation speed is as slow as deniable [18,21,22,25,29,30,33,38,40,[45][46][47][48][49][50][51]. The dynamic integration applies the likelihood of road closures or car accidents as a result of flooding [25,29,38,46,49,50].…”

“…Macroscopic traffic modeling, a common approach in this field, is capable of representing congestion or diversion in a large area but cannot represent individual vehicles or people on the network. Many articles [18,22,25,27,29,32,35,38,45,49,53,58,59] used macrosimulation in their methods; however, it cannot properly address the dynamics of the transportation system during a flooding event. Nevertheless, the macroscopic modeling method has advanced in the field of traffic flow theory and control in the form of macroscopic fundamental diagrams (MFDs), which have been utilized by Mitsakis et al [29] and Suwanno et al [52].…”

“…Data in 10 min time-steps, providing both the intensity and the total rainfall amounts that were recorded during the storm Traffic data from 219 field detectors (inductive loops) of the Athens traffic management center, measuring vehicle flow, speed and occupancy, Data from detectors located within the same zone for MFDs (vehicle flow vs. vehicle speed vs. occupancy) analysis, Floating Car Data (FCD) from freight vehicle fleets containing GPS coordinates and speed information Suwanno et al [52] Data at 5 min intervals from the road flood monitoring system Taxi data, The road network for the case study of the Sukhumvit area, Traffic signal input Q. Li et al [18] The meteorological data containing hourly rainfall intensity observed from 44 weather stations Taxi GPS data include records of the date, time, license plate number, longitude, latitude, instantaneous speed, direction, and taxi status taken every 10 s, Road network as a directed graph Lu et al [45] Light detection and ranging digital elevation data…”