Impact of Rainfall on Traffic Speed in Major Cities of China

The construction of healthy transportation is an important ingredient for promoting the healthy development of cities. The establishment of an urban traffic evaluation mechanism can provide an important basis for the construction of healthy transportation. This study focused on the impact of precipitation on traffic speed and developed an urban traffic vulnerability index. This index reflects the degree of traffic affected by precipitation, which is calculated based on the traffic congestion index under different rainfall intensities. The traffic vulnerability indices of 41 major cities in China under rainfall conditions were evaluated. Based on the above traffic vulnerability indexes, the impact of socioeconomic factors on urban traffic vulnerability was analyzed. The three key findings of this study are as follows: there was a positive correlation between the vulnerability index and the gross domestic product (GDP); the urban population (POP) had a significant impact on the urban traffic vulnerability; and urban car ownership had little impact on traffic vulnerability. Based on these findings, possible measures to improve urban traffic vulnerability are proposed. The construction of an index system provides a basis for enhancing the urban traffic assessment mechanism, promoting the development of urban physical examinations and building healthy transportation and healthy cities.

A Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.

Spatiotemporal Analysis of Highway Traffic Patterns in Hurricane Irma Evacuation

The State of Florida is significantly vulnerable to catastrophic hurricanes that cause widespread infrastructural damage and claim lives annually. In 2017, Hurricane Irma, a Category 4 hurricane, took on the entirety of Florida, causing the state’s largest evacuation ever as 7 million residents fled the hurricane. Floridians fleeing the hurricane faced the unique challenge of where to go, since Irma made an unusual landfall from the south, enveloping the entire state, forcing evacuees to drive farther north, and creating traffic jams along Florida’s evacuation routes that were worse than during any other hurricane in Florida's history. This study aimed to assess the spatiotemporal traffic impacts of Irma on Florida’s major highways based on real-time traffic data before, during, and after the hurricane made landfall. First, we conducted a time-series-based analysis to evaluate the temporal evacuation patterns of this large-scale evacuation. Second, we developed a metric, namely the congestion index (CI), to assess the spatiotemporal evacuation patterns on I-95, I-75, I-10, I-4, and turnpike (SR-91) highways with a focus on both evacuation and returning traffic. Third, we employed a geographic information system-based analysis to visually illustrate the CI values of corresponding highway sections with respect to different dates and times. Findings clearly showed that imperfect forecasts and the uncertainty surrounding Irma’s predicted path resulted in high levels of congestion and severe delays on Florida’s major evacuation routes.

Development of congestion index model and analysis of mitigation measures on urban arterials using microsimulation

Traffic congestion is one of the main issues related to traffic engineering, planning, and policies that directly influence the economy, environment, and lifestyle in developing countries, particularly in India. This study's main objectives are to establish the congestion index model and analyze the various mitigation measures using microsimulation. The required data were collected from selected two corridors in Tiruchirappalli, India. A correlation test was performed to identify the significant parameters that influence traffic congestion. Congestion Indices and Travel time reliability measures were computed to quantify congestion. Using a conventional regression technique, the congestion index model was developed to predict the congestion levels. The validated model predicts the congestion accurately. The mentioned statistical tests and model development were performed in SPSS 25.0 software at 95% confidence interval. A VISSIM based microsimulation was calibrated and simulated various mitigation measures with possible scenarios. Thus, the mind-numbing traffic jams can be reduced, and as a result, a great loss for the Indian economy can be reduced.