Impact of Rural Roads in India: A Case Study Based on Spatial Data Analysis

The Government of India launched its National Rural Roads Program known as Pradhan Mantri Gram Sadak Yojana (PMGSY) to connect the 167 thousand unconnected villages in the country by all-weather roads to improve connectivity there. It is important to study the impact of such intervention on various socio-economic indicators of rural development there. This study assesses the impact of those roads on the different aspects of rural community. The assessment has been completed based on spatial visualisation of the impact created by various facility parameters in rural development using various questionnaires formed and applied on a few selected blocks. Spatial data was collected and integrated using open-source software (QGIS) and statistical analysis has been performed to understand the percentage change in socio-economic indicators related to education, healthcare, agriculture, marketing and employment opportunities which are essential elements of the integrated rural development in India. The analysis appears helpful in estimating the sensitivity of government policies in the context, and thus understanding the requirement of policy changes and implementation in rural India.

Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy

Evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, evaluation and analysis of important contributing factors affecting the number of crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA) are employed for evaluating the number of vehicles involved in the accident based on the seven factors affecting transport safety including the Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS) and Annual average daily traffic (AADT) of rural roads of Cosenza in southern Italy. In this study, 564 data sets of rural areas were investigated and relevant effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation for rural areas. The results obtained demonstrated that "Average speed" has the highest level and "Weekday" has the lowest level of importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

Driving Simulator Study for Evaluating the Effectiveness of Virtual Warnings to Improve the Safety of Interaction Between Cyclists and Vehicles

Globally, cyclists account for 3% of all road traffic deaths, with the highest percentage occurring in Europe (8%) where the bicycle is considered a true alternative mode of transport. Among the causes of crashes are vehicles overtaking cyclists, especially on rural roads. In this study, a new application of augmented reality (AR) warnings for connected vehicles is tested by means of a driving simulator. The overall objective of the study consists in assessing the effectiveness of three proposed AR systems in improving the safety of interactions between vehicles and cyclists, especially during overtaking maneuvers. The AR systems were tested on a sample of 46 drivers and provided them with additional virtual visual information aimed at improving the driver’s risk perception and assessment of safe distance from a cyclist. The virtual warning configurations were: (i) a yellow safety zone around the cyclist; (ii) a color-changing safety zone that changes from red to green when the driver has safe lateral space to overtake the cyclist; (iii) the same color-changing security zone but with an additional acoustic warning. The AR warnings were found to be quite effective as they helped drivers overtake cyclists more safely. With AR warnings (especially with the additional audio), it was found that drivers adopted longer distances from cyclists and entered the oncoming lane less frequently, thus lowering the risk of collision with cyclists as well as the risk of head-on collision with oncoming vehicles.

Evaluation of the Radar Speed Cameras and Panels Indicating the Vehicles’ Speed as Traffic Calming Measures (TCM) in Short Length Urban Areas Located along Rural Roads

Traffic calming measures (TCMs) are implemented in urban areas to reduce vehicles’ speed and, generally speaking, results are obtained. However, speed is still a problem in rural roads crossing small villages without a bypass and with short-length urban areas, since drivers do not normally reduce their speed for that short segment. Hence, various TCM can be installed. It is necessary to maintain a calm area in these short segments to improve road safety, especially for pedestrian aiming to cross the road, and to save combustible by avoiding a constant increase-decrease of speed. Four villages were selected to evaluate the efficiency of radar speed cameras and panels indicating vehicle’s speed. Results showed that the presence of radar speed cameras reduces the speed in the direction they can fine, but with a lower effect in the non-fining direction. Additionally, a positive effect was observed in the fining direction in other points, such as pedestrian crossings. Nevertheless, the effect does not last long and speed cameras may be considered as punctual measures. If the TCMs are placed far from the start of the village they are not respected. Hence, it is recommended to place them near the real start of the build-up area. Lastly, it was verified that longer urban areas make overall speed decrease. However, when drivers feel that they are arriving to the end of the urban area, due to the inexistence of buildings, they start speeding up.

Driver Behavior When Overtaking Cyclists Riding in Different Group Configurations on Two-Lane Rural Roads

The presence of cyclists on Spanish rural roads is ever increasing and currently frequent, and thus becoming a serious safety concern. In rural environments, the risk of a crash is higher than in rural areas. The main cause is the higher speed of motor vehicles during overtaking manoeuvres. This manoeuvre is especially challenging when cyclists ride in groups as they may change size, length, shape, and speed along their route. These variables and those related to road cross-section can influence driver behaviour when overtaking a group of cyclists. To study this, instrumented bicycles were used to ride along five road segments with different geometric and traffic characteristics. Cyclists rode individually and in groups. Overtaking was evaluated by analysing the lateral distance, the speed, and other characteristics of the manoeuvre. Wider roads presented higher lateral clearances and overtaking speeds. Narrower roads had a high opposing lane invasion but a high level of compliance with the minimum lateral clearance. A higher clearance and lower speed of overtaking vehicles was registered when cyclists rode in line. Compliance with the 1.5 m clearance depended on the group configuration, being higher when cyclists rode in line. However, overtaking cyclists riding two abreast presented more accelerative manoeuvres, especially on narrow roads.

Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions

Appropriate driving technique, in compliance with eco-driving principles, remains an effective method to reduce fuel consumption. The selection of the correct gear is one of the pertinent factors when driving a car with a manual gearbox. In this study we have analyzed fuel overconsumption based on data recorded in real traffic conditions for vehicles driven by experienced drivers, using a black-box model. It was found that the total share of trip time with a lower than optimal gear selected amounted to from c.a. 3% for motorway driving up to 28% on rural roads. The mean fuel consumption reduction factor (following selection of the next gear up) amounted to from c.a. 2% up to 20%, depending on the selected gear and type of driving. Unfortunately, the potential for reduction of fuel consumption is not evenly distributed over the entire operating area of the engine. Thus, the cumulative reduction of fuel consumption, due to selection of the optimal gear, amounted to from c.a. 0.2% for motorway driving up to 3–6%, for urban and rural driving. It was shown that due to the selection of the appropriate gear, there still exists a real possibility of reduction of fuel consumption, even in the case of experienced drivers.

Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data

First responders including firefighters, paramedics, and police officers are among the first to respond to vehicle collisions on roads and highways. Police officers conduct regular roadside Please check if the country name is correct traffic controls and checks on urban and rural roads, and highways. Once first responders begin such operations, they are vulnerable to motor vehicle collisions by oncoming traffic, a circumstance that calls for a better understanding of contributing factors and the extent to which they affect tragic outcomes. In light of factors identified in the literature, this paper applies machine learning methods including decision tree and random forest to a subset of the National Collision Database (NCDB) of Canada that includes information on collisions between two vehicles (one in parked position) and the severity of these collisions as measured by having or not having injuries. Findings reveal that key measurable, predictable, and sensible factors such as time, location, and weather conditions, as well as the interconnections among them, can explain the severity of collisions that may happen between motor vehicles and first responders who are working alongside the roads. Analysis from longitudinal data is rich and the use of automated methods can be used to predict and assess the risk and vulnerability of first responders while responding to or operating on different roads and conditions.

Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.