scholarly journals Joint Impact of Rain and Incidents on Traffic Stream Speeds

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mohammed Elhenawy ◽  
Hesham A. Rakha ◽  
Huthaifa I. Ashqar
Keyword(s):  
Heavy Rain ◽  
Weather Conditions ◽  
Free Flow ◽  
Highway Traffic ◽  
Speed Reduction ◽  
Light Rain ◽  
The Road ◽  
Traffic Stream ◽  
Traffic Speed ◽  
The Relationship

Unpredictable and heterogeneous weather conditions and road incidents are common factors that impact highway traffic speeds. A better understanding of the interplay of different factors that affect roadway traffic speeds is essential for policymakers to mitigate congestion and improve road safety. This study investigates the effect of precipitation and incidents on the speed of traffic in the eastbound direction of I-64 in Virginia. To the best of our knowledge, this is the first study that studies the relationship between precipitation and incidents as factors that would have a combined effect on traffic stream speeds. Furthermore, using a mixture model of two linear regressions, we were able to model the two different regimes that the traffic speed could be classified into, namely, free-flow and congested. Using INRIX traffic data from 2013 through 2016 along a 25.6-mi section of Interstate 64 in Virginia, results show that the reduction of traffic speed only due to incidents ranges from 41% to 75% if the road is already congested. In this case, precipitation was found to be statistically insignificant. However, regardless of the incident impact, the effect of light rain in free-flow conditions ranges from insignificant to a 4% speed reduction while the effect of heavy rain ranges from a 0.6% to a 6.5% speed reduction when the incident severity is low but has a roughly double effect when the incident severity is high.

Investigating the Impact of Rainfall on Travel Speed

2014 ◽  
Vol 71 (3) ◽  
Author(s):  
Nordiana Mashros ◽  
Johnnie Ben-Edigbe ◽  
Hashim Mohammed Alhassan ◽  
Sitti Asmah Hassan
Keyword(s):  
Empirical Studies ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Travel Speed ◽  
Operating Conditions ◽  
Speed Reduction ◽  
Light Rain ◽  
Rainfall Condition ◽  
Adverse Weather ◽  
The Impact

The road network is particularly susceptible to adverse weather with a range of impacts when different weather conditions are experienced. Adverse weather often leads to decreases in traffic speed and subsequently affects the service levels. The paper is aimed at investigating the impact of rainfall on travel speed and quantifying the extent to which travel speed reduction occurs. Empirical studies were conducted on principle road in Terengganu and Johor, respectively for three months. Traffic data were collected by way of automatic traffic counter and rainfall data from the nearest raingauge station were supplied by the Department of Irrigation and Drainage supplemented by local survey data. These data were filtered to obtain traffic flow information for both dry and wet operating conditions and then were analyzed to see the effect of rainfall on percentile speeds. The results indicated that travel speed at 15th, 50th and 85th percentiles decrease with increasing rainfall intensities. It was observed that allpercentile speeds decreased from a minimum of 1% during light rain to a maximum of 14% during heavy rain. Based on the hypothesis that travel speed differ significantly between dry and rainfall condition; the study found substantial changes in percentile speeds and concluded that rainfalls irrespective of their intensities have significant impact on the travel speed.

scholarly journals Fail to Yield? An Analysis of Ambulance Crashes in Taiwan

2021 ◽  
Vol 13 (3) ◽  
pp. 1566
Author(s):  
Rong-Chang Jou ◽  
Ming-Che Chao
Keyword(s):  
Public Education ◽  
Weather Conditions ◽  
Medical Emergency ◽  
Transportation Safety ◽  
Emergency Vehicle ◽  
The Road ◽  
Mnl Model ◽  
On The Road ◽  
Crash Injuries ◽  
The Relationship

Introduction—Medical emergency vehicles help patients get to the hospital quickly. However, there were more and more ambulance crashes on the road in Taiwan during the last decade. This study investigated the characteristics of medical emergency vehicle crashes in Taiwan from January 2003 to December 2016. Methods—The ordered logit (OL) model, multinominal logit (MNL) model, and partial proportional odds (PPO) model were applied to investigate the relationship between the severity of ambulance crash injuries and its risk factors. Results—We found the various factors have different effects on the overall severity of ambulance crashes, such as ambulance drivers’ characteristics and road and weather conditions. When another car was involved in ambulance crashes, there was a disproportionate effect on the different overall severity, as found by the PPO model. Conclusions—The results showed that male ambulance drivers and car drivers who failed to yield to an ambulance had a higher risk of severe injury from ambulance crashes. Ambulance crashes are an emerging issue and need further policies and public education regarding Taiwan’s ambulance transportation safety.

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

2021 ◽  
Vol 10 (8) ◽  
pp. 557
Author(s):  
Qiuping Li ◽  
Haowen Luo ◽  
Xuechen Luan
Keyword(s):  
Traffic Management ◽  
Urban Transportation ◽  
Three Dimensional ◽  
Heavy Rain ◽  
The Road ◽  
Road Surfaces ◽  
Speed Change ◽  
The Impact ◽  
The Relationship ◽  
Personal Travel

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.

Snow and Ice Control System Based on Slipperiness Data Transmitted by Drivers: Usefulness of Subjective Slipperiness Data

1996 ◽  
Vol 1533 (1) ◽  
pp. 44-49
Author(s):  
Takashi Nakatsuji ◽  
Takashi Fujiwara ◽  
Toru Hagawara ◽  
Yuki Onodera
Keyword(s):  
Control Systems ◽  
Weather Conditions ◽  
Friction Coefficients ◽  
The Road ◽  
The Third ◽  
Road Surfaces ◽  
Road Condition ◽  
The Relationship ◽  
Ice Control ◽  
Snow And Ice

In Japan, the regulation of studded tires requires the establishment of new countermeasures for effective ice control on slippery roads in winter. The most important information for snow and ice control systems is determining the slipperiness of road surfaces. To detect the slipperiness simply and precisely, a monitoring system was examined in which drivers judged the slipperiness. To evaluate the suitability of such slipperiness data, three investigations were carried out: (a) the relationship between the road condition classification and the slipperiness index, (b) the effectiveness of the subdivision of road classification, and (c) the comparison of slipperiness indexes with the actual friction coefficients. To address the first problem, the road conditions were investigated for 1 month with the cooperation of 10 taxi companies. It was found that the subjective slipperiness index was more sensitive to changes in weather conditions than the road classifications, and that icy roads do not always correspond to slippery roads. That is, there was a limitation on expressing road conditions by road classification. For the second problem, a similar investigation was performed by subdividing the road conditions into more classes. It was concluded that the subdivision of road classification is not so effective in precisely representing the slipperiness of roads. For the third problem, it was clarified that the subjective slipperiness indexes more or less agree with the actual friction coefficients. As for the results, the slipperiness index showed potential for use in snow and ice control systems.

Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Meenu ◽  
Anshu Sharma ◽  
Rahul Malhotra
Keyword(s):  
Circular Polarization ◽  
Free Space ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Detailed Comparison ◽  
Free Space Optical ◽  
Light Rain ◽  
Link Distance ◽  
Mode Division Multiplexing ◽  
Shift Keying

Abstract In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system.

scholarly journals The Extent of Capacity Loss Caused by Rainfall at Signalised Intersections

2020 ◽  
Vol 14 (1) ◽  
pp. 214-221
Author(s):  
J. Oyaro ◽  
J. Ben-Edigbe
Keyword(s):  
Rainfall Intensity ◽  
Heavy Rain ◽  
Speed Reduction ◽  
Light Rain ◽  
Traffic Conditions ◽  
Capacity Loss ◽  
Capacity Reduction ◽  
Saturation Flow ◽  
Intensity Study ◽  
Signalised Intersection

Background: Even though their physical characteristics exert a constant influence on capacity and saturation flows, signalized intersections are fixed facilities not affected by rainfall. Whilst traffic conditions with varying effects can be regulated, rainfall conditions cannot be regulated but compensated for by warning drivers to reduce speed. Speed reduction has an impact on signalised intersection capacity, whilst signalised intersection capacity is a function of saturation flow, effective green, and cycle time. In this paper, a capacity loss is the differential percentage between ‘with and without’ rainfall scenario. Aim: The paper investigated the extent of capacity loss caused by rainfall at signalised intersections. Methods: In Durban, South Africa, rainfall data were collected, collated, and correlated with traffic data in a 'with and without' rainfall intensity study. Rainfall intensity was classified according to the rate of precipitation as follows; rainfall intensity(i): light rain (i <2.5mm/h); Moderate rain (2.5mm/h ≤ i < 10mm/h), and heavy rain (10 ≤ i ≤ 50mm/h) as prescribed by the World Meteorological Society. Results: Empirical results show that rainfall intensity has an effect on road capacity at a signalised intersection. Generally, for the vehicles going straight, light rain caused a 4.25% capacity loss; moderate rain 9.18% while heavy rain caused an 11.53% capacity reduction. With right-turning vehicles, light rain caused 7.38% capacity loss; moderate rain caused 14.3%, while heavy rain accounted for 19.15% capacity reduction. Conclusion: The paper concluded that rainfall at signalised intersections would cause an anomalous capacity reduction. Since the database for the study is small, the paper advocates for further studies based on a broader database to include yellow interval time.

scholarly journals Evaluating the Combined Effects of Weather and Real-Time Traffic Conditions on Freeway Crash Risks

2018 ◽  
Vol 10 (4) ◽  
pp. 837-850
Author(s):  
Chengcheng Xu ◽  
Chen Wang ◽  
Pan Liu
Keyword(s):  
Risk Model ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Case Control ◽  
Environmental Information ◽  
Prediction Performance ◽  
Crash Risk ◽  
Light Rain ◽  
Traffic Conditions ◽  
Real Time Traffic

Abstract The study presented in this paper investigated the combined effects of environmental factors and real-time traffic conditions on freeway crash risks. Traffic and weather data were collected from a 35-km freeway segment in the state of California, United States. The weather conditions were classified into five categories: clear, light rain, moderate/heavy rain, haze, and mist/fog. Logistic regression models using unmatched case-control data were developed to link the likelihood of crash occurrences to various traffic and environmental variables. The sample size requirements for case-control studies and the interaction between traffic and environmental variables were considered. The model estimation results showed that the light rain, moderate/heavy rain, and mist/fog significantly increase freeway crash risks. The interaction between light rain and upstream occupancy was also found to be statistically significant. Bootstrap analyses were conducted to quantify the interaction effect between these two variables. The crash risk model was compared to a reduced model in which environmental information was not included. It was found that the inclusion of environmental information improved both goodness of fit and prediction performance of the crash risk prediction model. The inclusion of environmental information in crash risk models improved the prediction accuracy of crash occurrences by 6.8% and reduced the false alarm rate by 1.3%. It was also found that the inclusion of environmental information had minor impacts on the prediction performance of the crash risk model in clear weather conditions.

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