Effectiveness Analysis on Human-Machine Information Interaction of Intelligent Highway

Different human-machine collaboration modes and driving simulation tests with the orthogonal method considered are designed for a series of typical intelligent highway landscapes. The feedback of drivers under different interaction modes is evaluated through NASA-LTX questionnaire, driving simulator, eye tracker, and electroencephalograph (EEG). This optimal interaction mode (including voice form, broadcasting timing, and frequency) of each driving assistance scene in CVI (Cooperative Vehicle Infrastructure) environment under the conditions of high and low traffic is determined from subjective and objective perspectives. In accordance with feedback of these subjects on each set scene, the voice information structure of each assistance mode plays the most important role on drivers followed by the broadcasting timing and frequency. These broadcasts which provide good effects include scenarios such as various assistance scenes at curves and an early warning timing at a long-distance trip as well as a high early warning frequency; in addition, as for an exit-tip assistance scenario, a voice mode assistance is preferred; and for various speed assistance scenes, the beep mode is better. Furthermore, it is found that, at a higher traffic level but a short-distance trip, an early warning timing is favored generally for various scenes while under a low traffic level, a long-distance early warning timing is better.

A Sensor-Based Drone for Pollutants Detection in Eco-Friendly Cities: Hardware Design and Data Analysis Application

The increase in produced waste is a symptom of inefficient resources usage, which should be better exploited as a resource for energy and materials. The air pollution generated by waste causes impacts felt by a large part of the population living in and around the main urban areas. This paper presents a mobile sensor node for monitoring air and noise pollution; indeed, the developed system is installed on an RC drone, quickly monitoring large areas. It relies on a Raspberry Pi Zero W board and a wide set of sensors (i.e., NO2, CO, NH3, CO2, VOCs, PM2.5, and PM10) to sample the environmental parameter at regular time intervals. A proper classification algorithm was developed to quantify the traffic level from the noise level (NL) acquired by the onboard microphone. Additionally, the drone is equipped with a camera and implements a visual recognition algorithm (Fast R-CNN) to detect waste fires and mark them by a GPS receiver. Furthermore, the firmware for managing the sensing unit operation was developed, as well as the power supply section. In particular, the node’s consumption was analysed in two use cases, and the battery capacity needed to power the designed device was sized. The onfield tests demonstrated the proper operation of the developed monitoring system. Finally, a cloud application was developed to remotely monitor the information acquired by the sensor-based drone and upload them on a remote database.

Impact of Automated Vehicles Using Eco-Cruise Control on the Traffic Flow

The paper provides a detailed analysis of the impact of automated vehicles using eco-cruise control system on the traffic flow. The speed profiles of vehicles using eco-cruise control system generally differ from those of conventional human-driven vehicles. The characteristics of the traffic flow on macroscopic traffic level combine both automated and human-driven vehicles. In the simulation-based analysis the effects of traffic volume and the ratio of the automated vehicles are in the focus. Based on the results the analysis an extension of the eco-cruise control is also proposed, in which the balance between the traffic flow and transport efficiency is achieved.

Analyzing the Coordination Relationship between Urban Built Environment and Traffic Level

The coordination relationship between urban built environment and transport system is an indispensable field in the study of urban planning. Recent research efforts in built environment and transport system have focused on the effects of built environment on travel behaviors, such as car ownership, choice of travel mode, and travel frequency. These travel behaviors will affect the traffic level. However, research studies on direct assessments of links between built environment and traffic level are still limited. This paper aims to fill this gap by modeling with data envelopment analysis based on Point of Interest (POI) data and floating car data collected in Jinan, China. It is found that the coordination relationship between built environment and traffic level is poor in Jinan. With regard to the built environment input index, the distance from the city center has the greatest influence on the coordination relationship. And for the built environment output index, bus stop influences the coordination relationship most significantly. This research can support the provision of quantitative basis for the formulation of governance priorities for traffic governance policies.

Hidden Service Website Response Fingerprinting Attacks Based on Response Time Feature

It has been shown that website fingerprinting attacks are capable of destroying the anonymity of the communicator at the traffic level. This enables local attackers to infer the website contents of the encrypted traffic by using packet statistics. Previous researches on hidden service attacks tend to focus on active attacks; therefore, the reliability of attack conditions and validity of test results cannot be fully verified. Hence, it is necessary to reexamine hidden service attacks from the perspective of fingerprinting attacks. In this paper, we propose a novel Website Response Fingerprinting (WRFP) Attack based on response time feature and extremely randomized tree algorithm to analyze the hidden information of the response fingerprint. The objective is to monitor hidden service website pages, service types, and mounted servers. WRFP relies on the hidden service response fingerprinting dataset. In addition to simulated website mirroring, two different mounting modes are taken into account, the same-source server and multisource server. A total of 300,000 page instances within 30,000 domain sites are collected, and we comprehensively evaluate the classification performance of the proposed WRFP. Our results show that the TPR of webpages and server classification remain greater than 93% in the small-scale closed-world performance test, and it is capable of tolerating up to 10% fluctuations in response time. WRFP also provides a higher accuracy and computational efficiency than traditional website fingerprinting classifiers in the challenging open-world performance test. This also indicates the importance of response time feature. Our results also suggest that monitoring website types improves the judgment effect of the classifier on subpages.

Decision Trees for Selecting Asphalt Pavement Crack Sealing Method

Crack sealing is an important preventive treatment in the pavement preservation program. To achieve a cost-effective crack seal, it is crucial to select a proper crack sealing method. In Minnesota asphalt pavement cracks are sealed using both the clean-and-seal and rout-and-seal methods; however, there is no guideline for choosing the most suitable crack sealing method. This study deals with a literature review, an online survey, crack seal performance data collection, cost-effectiveness analysis of the crack sealing methods, life cycle cost analysis, and development of two decision trees to aid in selecting the most suitable crack sealing method. The first, which can be used in the pavement management system, needs information such as crack severity, pavement type (new versus overlay), pavement analysis period and design life, traffic level, and crack seal sequence (first, intermediate, or last). The second decision tree, which is a simplified version of the first and can be used by preventive maintenance crews, requires less information, such as crack severity, traffic level, and place in the crack sealing sequence.

Prediction and Potential Spatially Explicit Spread of COVID-19 in Mexico’s Megacity North Periphery

The novel COVID-19, detected in Wuhan, China, has reached almost every city across the globe, and researchers from many countries have used several epidemiologic models to describe the epidemic trends. In this context, it is also important to know the geographic extent of the infected population. Following this approach, a Gumpertz model was adapted with official data from the state of Hidalgo, Mexico, in order to estimate the people infected during this COVID-19 pandemic. We found, based on the adjusted data, the highest value in infected people according to official and theoretical data. Furthermore, using a geographical analysis based on geostatistical measures related to density of demographic and economic data, traffic level and geolocation, raster files were generated to estimate probability of coronavirus cases occurrence using the areas where the contagion may occur. We also distributed the maximum contagion obtained by the epidemic model, using these raster files, and a regression model to weight factors according their importance. Based on this estimated distribution, we found that most of the infected people were located in the southern border, a trend related to the economic strip in the southern part of Hidalgo State, associated with its vicinity to the Megacity of Mexico.

The Development of a CO2 Emission Coefficient for Medium- and Heavy-Duty Vehicles with Different Road Slope Conditions Using Multiple Linear Regression, and Considering the Health Effects

The current studies on carbon dioxide (CO2) emissions and the impacts on public health focus mainly on evaluating CO2 emissions from two types of emission sources. The first is a fixed source type from industrial plants, which can be controlled or easily evaluated. The second is a mobile source type from the transport sector, especially from medium- and heavy-duty vehicles (MHDVs), which produce high emissions. However, the common methods of evaluation of the average value of CO2 emissions per kilometer of a vehicle use a general Intergovernmental Panel on Climate Change (IPCC) model that does not consider the topography or road conditions. This affects the accuracy of CO2 emission assessments and, in turn, affects the accuracy of any analysis needed to establish health policies and the management of public health within the affected area. In this paper, therefore, we present the development of emission coefficient calculations with varying topography conditions for MHDVs with consideration of the health effects on the populace. The study area was the Asian highway network in Thailand that passes through all regions and is geographically diverse. Data were collected from the Department of Highway’s records, global positioning system (GPS) and electronic control unit (ECU) with data consisting of road data, slope, distance, traffic level and vehicle weight, as well as fuel consumption along the transportation route. To analyze and map out the correlation of the CO2 emission coefficients for each road slope, we determined the coefficient of the CO2 emissions using multiple linear regression analysis and validated this using Huber–White robust standard errors for heteroscedasticity. This method was more cost-efficient and time-efficient compared to the conventional approaches. We also created CO2 emission maps with risk points for health effects, and we propose policy designs and plans to manage the traffic level in each of these areas prone to higher levels of such emissions.

Evaluation of recycled asphalt mixture at low temperature using different analytical solutions

Using reclaimed asphalt pavement (RAP) in road infrastructures is crucial for mitigating the environmental impact while controlling the construction costs. However, poorer low temperature performance may be experienced for mixtures containing RAP. In this paper, the effect of RAP on the material response at low temperature is investigated through mixture creep testing with the bending beam rheometer. Three different mathematical approaches are selected for further evaluation in combination with simple statistical analysis. Based on the experimental data, creep stiffness, m-value, relaxation modulus, thermal stress, and critical cracking temperature are computed and compared. As a result, no differences are found between the virgin mixture and that designed with 15% of RAP. Poorer performance is observed when more than 25% of RAP is incorporated; however, no significant variation was observed for a further increase up to 40% suggesting that higher amount RAP could be used depending on traffic level and climate.