Noise is a major source of pollution that can affect the human physiology and living environment. According to the World Health Organization (WHO), an exposure for longer than 24 hours to noise levels above 70 dB(A) may damage human hearing sensitivity, induce adverse health effects, and cause anxiety to residents nearby roadways. Pavement type with different roughness is one of the associated sources that may contribute to in-vehicle noise. Most previous studies have focused on the impact of pavement type on the surrounding acoustic environment of roadways, and given little attention to in-vehicle noise levels. This paper explores the impacts of different pavement types on in-vehicle noise levels and the associated adverse health effects. An old concrete pavement and a pavement with a thin asphalt overlay were chosen as the test beds. The in-vehicle noise caused by the asphalt and concrete pavements were measured, as well as the drivers’ corresponding heart rates and reported riding comfort. Results show that the overall in-vehicle sound levels are higher than 70 dB(A) even at midnight. The newly overlaid asphalt pavement reduced in-vehicle noise at a driving speed of 96.5 km/hr by approximately 6 dB(A). Further, on the concrete pavement with higher roughness, driver heart rates were significantly higher than on the asphalt pavement. Drivers reported feeling more comfortable when driving on asphalt than on concrete pavement. Further tests on more drivers with different demographic characteristics, along highways with complicated configurations, and an examination of more factors contributing to in-vehicle noise are recommended, in addition to measuring additional physical symptoms of both drivers and passengers.Implications: While there have been many previous noise-related studies, few have addressed in-vehicle noise. Most studies have focused on the noise that residents have complained about, such as neighborhood traffic noise. As yet, there have been no complaints by drivers that their own in-vehicle noise is too loud. Nevertheless, it is a fact that in-vehicle noise can also result in adverse health effects if it exceeds 85 dB(A). Results of this study show that in-vehicle noise was strongly associated with pavement type and roughness; also, driver heart rate patterns presented statistically significant differences on different types of pavement with different roughness.

Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.

A work zone is an area of roadway with construction, maintenance, or utility work activities. According to NHTSA, many traffic accidents and fatalities happen in work zones. Many traditional safety countermeasures have been utilized in work zones, including installing special signs and barriers and posting suitable speed limits. However, these measures have not fully solved the problem. Radio frequency identification (RFID) is a powerful, globally accepted technology that has had great success in business management for decades and can be employed as an advisory device that communicates between vehicles and infrastructure in work zones. The purpose of this study was to develop an RFID-based driver smart assistance system (DSAS) to improve traffic safety and air quality in work zones. The RFID device enabled communication between the vehicle and the roadside on a real-time basis and was supplemented with GPS and other sensors for dynamic traffic management. Such an assistance system would provide suitable verbal and visual warning messages to drivers approaching a work zone. Road tests with the DSAS were conducted in Houston, Texas, with 20 drivers, and the system's impact on vehicle speeds, safety, and vehicle emissions was examined. Statistical results from the tests showed that the DSAS helped drivers take action earlier to decelerate and reduce speeds. All test subjects evaluated the system as satisfactory. This system can also help to reduce most types of vehicle emissions. Further testing and improving of this promising system is recommended in broader fields.