Analysis of Driving Control Characteristics in Typical Road Types

Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained.

Changes in Soil’s Chemical and Biochemical Properties Induced by Road Geometry in the Hyrcanian Temperate Forests

Forest roads play an important role in providing access to forest resources. However, they can significantly impact the adjacent soil and vegetation. This study aimed to evaluate the effects of road geometry (RG) on the chemical and biochemical properties of adjacent soils to assist in environmentally friendly forest road planning in mountainous areas. Litter layer, canopy cover, soil organic carbon (SOC) stock, total nitrogen (TN), the activity of dehydrogenase (DHA), and urease (UA) enzymes at a 0–20 cm soil depth were measured by sampling at various distances from the road edge to 100 m into the forest interior. The measurements were done for three road geometries (RG), namely straight, curved, and bent roads, to ensure data heterogeneity and to reflect the main geometric features of the forest roads. Analysis of variance (ANOVA) showed that the effects of RG on the measured variables were statistically significant. Spearman’s correlation test clearly showed a strong positive correlation between environmental conditions, SOC, TN, DHA, and UA for given RGs. Based on piecewise linear regression analysis, the down slope direction of the straight and the inside direction of bent roads accounted for the lowest and highest ranges of ecological effects, respectively. The results of this study contribute to our understanding of the environmental effects brought about by road geometry, which can be important for forest road managers when applying the best management practices.

Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder–decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles’ trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.

Disaster and Risk Mitigation at The Toll Road Planning Stage

PT. Hutama Karya (Persero), according to Presidential Regulation No. 100 of 2014 and No. 117 of 2015, obtained an assignment to construct and operate 24 sections of Trans Sumatera Toll Road along 2,789 km, including Padang - Pekanbaru Toll Road, where almost all of the segments are located in fault areas and in areas with diverse rock formation. In terms of the number of fault locations, the toll road has a greater risk of earthquakes. Whereas in terms of varying rock formations, construction planning and improper structure determination will cause a highly cost-enhancing effect. In the planning stage, the selection of route is one of the mitigations to minimize the risks and impacts of the earthquake disaster. Toll road trajectories are designed optimally by considering the movement of the earth’s plates based on fault data on these locations and data on rock formations for the construction and structure plan of the Toll Road. Input data needed is Geological Secondary Data and Topographic Data containing information on fault areas and rock formations. Therefore, planning with Quantm Trimble software is the right solution. Determining the route with Quantm Trimble software is one of the effective and efficient methods. The main key in determining routes by Quantm Trimble is the software algorithm which can determine the route quickly by considering the main constraints such as avoiding fault areas, avoiding an area with certain rock formations also determining the construction methods on certain rock formation areas. Quantm Trimble software is able to generate several alternative routes based on user-defined constraints, including accommodating the automatic selected smoothing process according to the specified road geometry standard. The software greatly accommodates the determination of the plan by considering risk and disaster management, as well as being able to manage costs well by determining the construction method plan quickly and accurately.

Risk Prediction for Winter Road Accidents on Expressways

Road accidents caused by weather conditions in winter lead to higher mortality rates than in other seasons. The main causes of road accidents include human carelessness, vehicle defects, road conditions, and weather factors. If the risk of road accidents with changes in road weather conditions can be quantitatively evaluated, it will contribute to reducing the road accident fatalities. The road accident data used in this study were obtained for the period 2017 to 2019. Spatial interpolation estimated the weather information; geographic information system (GIS) and Shuttle Radar Topography Mission (SRTM) data identified road geometry and accident area altitude; synthetic minority oversampling technique (SMOTE) addressed the data imbalance problem between road accidents due to weather conditions and from other causes, and finally, machine learning was performed on the data using various models such as random forest, XGBoost, neural network, and logistic regression. The training- to test data ratio was 7:3. Random forest model exhibited the best classification performance for road accident status according to weather risks. Thus, by applying weather data and road geometry to machine learning models, the risk of road accidents due to weather conditions in the winter season can be predicted and provided as a service.

Car Following Behaviours on Multilane Highways in Kuwait: A Case Study on Road 40 during Winter Season

The car following behaviour of a driver is the process of following the drivers to create an adjustment in the leading vehicle behaviour. In a condition, where the traffic volume is in a free-flowing situation, the selection of vehicles speed is typically limited by some factors such as weather conditions, lighting, and road geometry features. This study aims to investigate the effects of climates on driver’s car following behaviour and speed flow relationships for highways in Kuwait. The case study was conducted at Road 40 in Kuwait using RTMS Sx-300 device which is known as a radar device particularly used for monitoring traffic. The data was gathered between the periods from 29th December 2018 to 5th January 2019 within winter. MATLAB code was written to analyse and classify the gathered data. Then, the models were built using R-software. The study depicts that nearly 24.87% of the vehicles move between 60 km/h and 69 km/hour. Additionally, the vehicles were segmented according to their types i.e., Truck, Small, Medium and Large Sized Cars, in order to find the impact of following pattern on the vehicle average. It has been found that no significant association remains amidst the type of following pattern and the headway. Ultimately, a liner regression of data was developed to calculate a liner equation that shows the average headway as an element of speed for sixteen diverse following patterns. It has been recognized that an association could be supposed in medium-sized and small-sized vehicles. It has been observed that headway average could be placed in a linear equation for large, medium, and small as well as truck vehicles. It is worthy to denote that when data is bigger, the exactitude of a study enhances. Findings from each model of liner regression has more than 80% confidence level. The models of regression are deliberated as statistically significant where, the R (square) figures lies amidst 0.99 till 0.6. As per the findings, speed is the key influencing factor for headway value. The type of car does affect headway with drivers behind Heavy Good Vehicles and cars at the similar speed. According to the data, cars are identified to keep more headway when behind Heavy Good Vehicles in contrast with when behind other cars. These results will help the drivers to understand their behaviour that are associated with car crashes. Thus, increase road safety awareness and reduce traffic congestion in Kuwait.

Monitoring of Carriageway Cross Section Profiles on Forest Roads: Assessment of an Ultrasound Data Based Road Scanner with TLS Data Reference

Forest roads are an important element in forest management as they provide infrastructure for different forest stakeholder groups. Over time, a variety of road assessment concepts for better planning were initiated. The monitoring of the surface cross-section profile of forest roads particularly offers the possibility to take early action in restoring a road segment and avoiding higher future costs. One vehicle-based monitoring system that relies on ultrasound sensors addresses this topic. With advantages in its dirt influence tolerance and high temporal resolution, but shortcomings in horizontal and vertical measuring accuracy, the system was tested against high resolution terrestrial laser scanner (TLS) data to find and assess working scenarios that fit the low- resolution measuring principle. In a related field test, we found low correct road geometry interpretation rates of 54.3% but rising to 91.2% under distinctive geometric properties. The further applied line- and segment-based method used to transform the TLS data to fit the road scanner measuring method allows the transfer of the road scanner evaluation principle to point-cloud or raster data of different origins.

Measurement of Thermal Comfort in Urban Public Spaces Semarang, Indonesia

Indonesia reported a maximum annual temperature rise of 0.3°C in urban regions. Semarang, the largest metropolitan city in the province of Central Java, is also experiencing an increase in temperature due to climate change therefore activities in urban public spaces are disrupted due to the absence of a comfortable temperature. Urban design elements, including land cover materials, road geometry, vegetation and traffic frequency expressed significant effects on micro-climate. Measurement of Thermal Comfort in Urban Public Spaces Semarang was carried out s at the micro level as an old historical district The Old Town and Chinatown. This increment indeed influences thermal comfort level in its outdoor environments which are important for comfortability of outdoor activity. This study aims to analyse surface temperature through Thermal Comfort Measurement. Data was obtained by measuring air temperature, wind speed and humidity in the morning, afternoon, and evening. Inverse distance weighted (IDW), thermal comfort calculations and micro-climate model were employed to evaluate existing physical conditions of these settlements. The results showed both Old Town and Chinatown observed thermal comfort value above 27°C and are categorized as uncomfortable for outdoor activities. This research is contributing to the need to further develop public spaces to potentially adapt to environmental changes.