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New Mexico's oil and natural gas industry has the second highest occupational fatality rate among oil and gas workers in the nation. There is currently limited data available regarding the top contributing factors to occupational mortality specific to the state's oil and gas industry. This study seeks to understand causes of mortality among oil and gas workers in New Mexico between 2008 and 2018. To facilitate this study, case reports were obtained from the New Mexico Office of the Medical Investigator, and population data was acquired from the US Department of Labor. In this 11-year span, there were 73 cases, with an average annual mortality rate of 37 deaths per 100,000 oil and gas workers. Leading causes of death were vehicle accidents (36%), cardiovascular incidents (22%), and crush injuries (19%). The majority of vehicle accidents involved single vehicle accidents, and correct seat belt use was only documented 23% of the time. The majority of cardiovascular deaths were due to arteriosclerotic and atherosclerotic cardiovascular disease. Alcohol was present in 18% of cases, and drugs were present in 19% of cases with methamphetamine present in 10% of investigated deaths. This is the first study to directly include cardiovascular incidents in the leading causes of death; otherwise, this study reflects national data reporting vehicle accidents and crush injuries as the leading causes of death. Going forward, prevention measures should effectively target safe driving practices focusing on seatbelt use, and mitigation of workplace drug and alcohol consumption.

In order to solve the problems of complex circuit debugging and inconvenient operation, wireless communication technology is adopted and the USR-WIFI232-B2 module is used to complete the data transmission and reception between the single vehicle simulation tester and the upper computer. The hardware circuit and software flow of the portable single vehicle simulation tester are described, the wireless communication module is configured, and the laboratory test and on-site debugging are completed. It greatly facilitates the operation of the workers and improves the debugging efficiency.

Commercial topical medications for oral conditions are scarce and the vehicles used are not very suitable. Therefore, formulations containing active ingredients in vehicles specially designed for oral application must often be prepared. Drug compounding offers other advantages, including the possibility of combining several active ingredients in a single vehicle and prescribing drugs that are in short supply. In this article, we describe the main pharmaceutical formulations used to treat the most common diseases of the oral mucosa.

Detecting multiple license plate numbers in crowded street scenes is challenging and requires the attention of researchers. In contrast to existing methods that focus on images that are not crowded with vehicles, in this work we aim at situations that are common and complex, for example, in city environments where numerous vehicles of different types like cars, trucks, motorbike etc. may present in a single image. In such cases, one can expect large variations in license plates in terms of quality, backgrounds, and various forms of occlusion. To address these challenges, we explore Adaptive Progressive Scale Expansion based Graph Attention Network (APSEGAT). This approach extracts local information which represents the license plates irrespective of vehicle types and numbers because it works at the pixel level in a progressive way, and identifies the dominant information in the image. This may include other parts of vehicles, drivers and pedestrians, and various other background objects. To overcome this problem, we integrate concepts of graph attention networks with progressive scale expansion networks. For evaluating the proposed method, we use our own dataset, named as AMLPR, which contains images captured in different crowded street scenes in different time span, and the benchmark dataset namely, UFPR-ALPR, which provides images of a single vehicle, and another benchmark dataset called, UCSD, which contains images of cars with different orientations. Experimental results on these datasets show that the method outperforms existing methods and is effective in detecting license plate numbers in crowded street scenes.

We consider a vehicle with two compartments that starts its route from a depot and visits N ordered customers in order to deliver new (or fresh or useful) products and to collect old (or expired or...

Single vehicle crashes, particularly those classified as run-off-the-road, are very common on two-lane rural highways. One method to potentially reduce such crashes is to provide additional driver information in the form of wider longitudinal edgeline pavement markings. However, since these markings deteriorate over time, the primary objective of this research was to study the effects of longitudinal edgeline pavement markings with varying deterioration levels and widths and to assess a driver’s ability to maintain lane position. The University of Idaho’s driving simulator was used to examine these effects by incorporating different marking deterioration percentages and roadway geometries on a two-lane rural highway environment. Two different pavement marking widths (4 and 6 inch) and four different deterioration levels (0%, 25%, 50%, and 75%) were assessed in daytime and nighttime conditions as part of this study. The results determined that while wider 6-in longitudinal edgeline pavement markings compared with standard four inch edgeline markings did not cause any significant changes in driver lane deviation during the day, statistically significant differences were observed in nighttime driving conditions. Drivers consistently maintained a lane position that slightly favored the edgeline side throughout the study and increasingly shifted away from the centerline as edgeline deterioration worsened.

To help reduce single vehicle run-off-road and two-lane two-way crossover crashes, Texas Department of Transportation (TxDOT), United States, has implemented various audible lane departure warning systems on seal coat road surfaces. This 20-month research project explored the effectiveness of these treatments using interior noise and vibration performance metrics and provided recommendations on implementation of these types of treatments. The researchers conducted performance evaluations at 24 unique field sites that had 51 treatments, and at a test deck that had 12 different variations of audible markings. The field sites consisted of varying designs and spacing of audible markings, varying spacing of rumble bars, and milled rumble strips. Researchers found that treatment effectiveness varies with vehicle type and vehicle speed. Traveling at higher speeds and in a vehicle with a stiffer suspension results in higher noise and vibration levels. The specific treatment design also impacts the performance. Treatments with closer spacing, longer bumps, and higher profiles produced higher noise and vibration levels. Certain alternative treatments were able to produce noise and vibration levels that approached levels of typical milled rumble strips. In areas where milled rumble strips cannot be used, these alternative treatments are viable options.

Serviceability performances of the long span bridge during strong winds, including the safety and comfort condition of driving road vehicle on the bridge, have attracted more and more attention in recent years. The dynamic behavior of the riding vehicle is essential for serviceability performance assessment. The vehicle-bridge coupling (VBC) simulation, featured as time-consuming, is traditionally applied, however, cannot meet the requirement for timely decision. Therefore, a monitoring-based approach, with the hybrid combination of in-situ structural health monitoring (SHM) data and dynamic simulation of single vehicle model, is presented in this paper to estimate the vehicle dynamic responses in a more efficient way. A numerical case study of a quarter vehicle model riding on a simply supported beam bridge is presented to validate the proposed approach. The first step is to investigate the feasibility of reproducing structural responses with the data from limited sensors using interpolation methods. Subsequently, dynamic responses of the riding vehicle are estimated using the monitoring-based approach. The influences of random traffic and wind loads are also considered. The estimated vehicle responses generally match VBC-based results, which proves reliability of the monitoring-based approach. Furthermore, the SHM data of a long-span bridge are utilized to investigate the potential usage of the proposed method. In particular, the dynamic responses of a popular light truck on the bridge and rigid road are respectively estimated and compared with each other. Results show that, among all the factors, i.e., bridge vibration, wind loads, and road roughness, the bridge vibration contributes the majority to the vertical vibration of the vehicle. For the lateral and torsional vibration of the vehicle, wind forces and bridge vibration are dominant factors. To summarize, the monitoring-based approach could help to estimate the dynamic responses of the riding vehicle on the bridge with a good reliability, regardless of the operation condition of the bridge, and provide timely and reliable information for serviceability and safety assessment.

Despite the vast body of literature on vehicle routing problems, little attention has been paid to multi-compartment vehicle routing problems that investigate transportation of different commodities on the same vehicle, but in different compartments. In this project, we present two strategically different versions of the MCVRP in general settings. In the first version, different commodities may be delivered to the customer by different vehicles, but the full amount of each product must be delivered by a single vehicle. In the second version, each customer may only be serviced by a single vehicle, which must deliver the full amount of all commodities demanded by that customer. We present a Branch-and-Price algorithm for solving the two versions of the problem to optimality and we analyze the effect of the strategic decision of whether or not to allow multiple visits to the same customer by comparing the optimal costs of the two versions. Computational results are presented for instances with up to 100 customers and the algorithm can solve instances with up to 50 customers and 4 commodities to optimality.NOTE: An early version of the paper was made public on the website of the journal. The present version is therefore denoted "Erratum".

Reliable estimation of vehicle speed is an active topic of research in the automotive industry and academia due to its technical challenges as well as applications to vehicle traction and stability control. In this direction, the emergence of new generations of communication technologies has brought new perspectives to traditional studies on vehicle speed estimation and control. To this end, this paper introduces a cooperative vehicle speed fault diagnosis and correction algorithm. The distributed part of the algorithm is based on a distributed function calculation algorithm for vehicle networks. The introduced algorithm enables each vehicle to gather some information from other vehicles in the network in a distributed manner and is robust to communication failures. A procedure to use such information for a single vehicle to diagnose and correct a possible fault in its own speed estimation/measurement is discussed. The functionality and performance of the proposed algorithms are verified via illustrative examples and simulation results.

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergency-related announcements is approximately 7 s.

In this paper, we present a theoretical model that solves the problem of minimization of aerial ropeway vehicle oscillations that are induced as the vehicle passes over a support. The task is formulated as an inverse problem, where the vehicle oscillations are minimized by an appropriate choice of the velocity profile of the hauling cable. We study two general cases numerically, a single vehicle system (FUNIFOR), as well as a classical aerial ropeway with two vehicles. In both cases we find optimal velocity profiles that show a considerable improvement of the oscillatory behavior of the vehicles as compared to constant velocity profiles and optimal profiles that have been obtained analytically by loosening some of the constraints for the system. In addition to a minimization of the vehicle oscillations, we also optimize the time that elapses as the vehicle is hauled through the system. We believe that this exploratory study lays a sound basis for various possible future studies and practical applications (Computer Aided Engineering).

Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

Introduction Recent research suggests that COVID-19 associated stay-at-home orders, or shelter-in-place orders, have impacted intra-and-interstate travel as well as motor vehicle crashes (crashes). We sought to further this research and to understand the impact of the stay-at-home order on crashes in the post order period in Connecticut. Methods We used a multiple-comparison group, interrupted time-series analysis design to compare crashes per 100 million vehicle miles traveled (VMT) per week in 2020 to the average of 2017–2019 from January 1-August 31. We stratified crash rate by severity and the number of vehicles involved. We modeled two interruption points reflecting the weeks Connecticut implemented (March 23rd, week 12) and rescinded (May 20th, week 20) its stay-at-home order. Results During the initial week of the stay-at-home order in Connecticut, there was an additional 28 single vehicle crashes compared to previous years (95% confidence interval (CI): [15.8, 36.8]). However, the increase at the order onset was not seen throughout the duration. Rescinding the stay-at-home order by and large did not result in an immediate increase in crash rates. Crash rates steadily returned to previous year averages during the post-stay-at-home period. Fatal crash rates were unaffected by the stay-at-home order and remained similar to previous year rates throughout the study duration. Discussion The initial onset of the stay-at-home order in Connecticut was associated with a sharp increase in the single vehicle crash rate but that increase was not sustained for the remainder of the stay-at-home order. Likely changes in driver characteristics during and after the order kept fatal crash rates similar to previous years.

This paper deals with a problem where the same vehicle performs several routes to serve a set of customers and arc traversal times vary along the planning horizon. The relationship with its time-invariant counterpart is investigated and a procedure to compute lower and upper bounds on the optimal solution value is developed. Computational results on instances, based on the Paris (France) road graph, show the effectiveness of this approach.

Common domain shift problem formulations consider the integration of multiple source domains, or the target domain during training. Regarding the generalization of machine learning models between different car interiors, we formulate the criterion of training in a single vehicle: without access to the target distribution of the vehicle the model would be deployed to, neither with access to multiple vehicles during training. We performed an investigation on the SVIRO dataset for occupant classification on the rear bench and propose an autoencoder based approach to improve the transferability. The autoencoder is on par with commonly used classification models when trained from scratch and sometimes out-performs models pre-trained on a large amount of data. Moreover, the autoencoder can transform images from unknown vehicles into the vehicle it was trained on. These results are corroborated by an evaluation on real infrared images from two vehicle interiors., Comment: This paper has been accepted at IEEE Intelligent Vehicles Symposium (IV), 2021

Air pollution and in particular PM2.5 emissions are a major problem worldwide. Road transport is a significant contributor to PM2.5 emissions in urban areas and as such it is important to understand and be able to accurately model the effects of vehicles on PM2.5 emissions. In this paper a computer vision algorithm is introduced which is able to extract vehicle trajectories from video footage. The algorithm has a 100% accuracy for overall total vehicle counting. Comparing the speeds predicted by the computer vision script to manually following a single vehicle feature on the video file, the average relative speed accuracy is 2.7% at a 1 Hz time resolution. Using these vehicle trajectories in an instantaneous vehicle emissions model and also as input to COPERT v5, tailpipe PM2.5 emissions were estimated and compared to on-road measurements. It was shown that a local sensor is not sufficient to determine vehicle tailpipe emissions due to the influence of meteorological conditions and other emission sources. Combining computer vision with an instantaneous vehicle emissions model is a useful method to evaluate changes in emissions caused by transport policies.

Vehicle-type specific injury severity has rarely been investigated mainly because of a lack of such information in hospital-based studies that normally exclude those who are severely injured and die on the scene. No study has been conducted either on driver characteristics in single vehicle crashes in Taiwan according to vehicle type. This was the first population-based study aiming to describe demographic characteristics in association with vehicle-specific rates of injury and fatality among driver victims involved in single-vehicle crashes in Taiwan. We presented sex and age-specific number and proportion of driver victims according to vehicle type. We calculated sex and age-specific rates of injury and fatality. Injury and fatality rates were also graphically presented. Bicycle and motorcycle rider victims generally had higher injury rates but lower fatality rates. However, older (45+) bicycle rider victims had greater fatality risk. By contrast, truck and car driver victims were generally associated with lower injury rates but with higher fatality rates. Elderly (65+ years) truck driver victims suffered from higher rates of injury and fatality. Male victims were found to have a higher fatality rate than female victims regardless of vehicle type. The vehicle-type-specific analyses of injury and fatality are considered useful in identifying single-vehicle crash victims at greater risks of injury and fatality.

Diagnostic Trouble Codes (DTCs) allow monitoring a wide range of fault conditions in heavy trucks. Ideally, a perfectly healthy vehicle should run without any active DTCs; in practice, vehicles often run with some active DTCs even though this does not pose a threat to their normal operation. When a DTC becomes active, it is therefore unclear whether it should be ignored or considered as a serious issue. Recent approaches in machine learning, such as training Variational Autoencoders (VAEs) for anomaly detection, do not help in this respect, for a number of reasons that we discuss based on actual experiments. In particular, a VAE tends to learn that a frequently active DTC is of no importance, when in fact it should not be dismissed completely; instead, such DTC should be assigned a relative weight that is smaller but still non-negligible when compared to other, more serious DTCs. In this work, we present an approach to measure the relative weights of active DTCs in a way that allows the analyst to prioritize them and focus on the most important ones. The approach is based on the concept of binary cross-entropy, and finds application not only in the analysis of DTCs from a single vehicle, but also in monitoring active DTCs across an entire fleet.

Autonomous vehicle platoon is a promising paradigm towards traffic congestion problems in the intelligent transportation system. However, under certain circumstances, the advantage of the platoon cannot be fully developed. In this paper, we focus on the highway Electronic Toll Collection (ETC) charging problem. We try to let the opportunistic platoon pass the ETC as a whole. There are three main issues in this scenario. Firstly, the opportunistic platoon is temporarily composed; vehicles do not trust each other. Secondly, single vehicle may try to escape from the ETC charging by following the platoon. Finally, platoon members may collude with each other and try to underreport the number of vehicles in the platoon so as to evade payment. To solve these challenges, we propose a blockchain-based efficient highway toll paradigm for the opportunistic platoon. The driving history, credential information of every registered vehicle, is recorded and verified from the blockchain. A roadside unit (RSU) is adopted to distinguish the single vehicle from the platoon and in charge of lane allocation. Additionally, an aggregate signature is introduced to accelerate the authentication procedure in the RSU. We analyse the potential security threats in this scenario. The experimental result indicates that our scheme is efficient and practical. Agency for Science, Technology and Research (A*STAR) Published version This work is supported by the IAF-PP WP5 : Design of Security Functionality for V2X Networks Grant for the project of A19D6a0053 by the Agency for Science, Technology and Research, Singapore.

Lane change is one of the most common maneuvers in traffic and at least 60,000 people are injured from them. This problem is usually addressed from the perspective of automation and for a single vehicle using path planning and control approaches. However, traffic is a multi-agent system which can use communication technologies to perform coordinated maneuvers in a decentralized manner. This paper aims to present a Decentralized Bargaining Negotiation Process allowing that two conflicting agents negotiate. During the negotiation, agents calculate velocity change proposals and communicate with each other in order to solve the conflict at the lowest cost. This enables that agents open safe distance gaps and make the maneuvers. We also proposed a cost function attenuation so that the agents can continue to negotiate while the agreement was not reached, but with less impact on the cost. We simulated lane merge task on SUMO with a two-lane highway with speed limit of 30 m/s. We initially simulated 2 agents and we were increasing the number of agents up to 26. We recorded the total average time for the platoon to perform the lane merge task, the platoon average velocity and the average of the speed limit exceeded in each simulation. By increasing the number of agents the total average time stabilized approximately 25 seconds. Which represents an expressive result because the time for one vehicle to complete the maneuver is 4.6 seconds. The average speed was also approximately constant around 22 m/s, which represents a good result for the traffic flow. The highest speed exceeded 9 % which represents 2.70 m/s with the attenuation cost function. We can observe that the approach proved to be scalable since the analyzed data show stability by increasing the number of vehicles.

ObjectiveTo examine the characteristics of seizure-related vehicle crashes (SRC).MethodUsing a nested case-control design, we identified and compared cases of SRC involving confirmed epilepsy patients with 137,126 non-seizure-related crash controls (NSRC) in the Australian state of Victoria. SRC were identified from approximately 20,000 epileptologist medical records by cross-referencing this source with the Victorian Police Traffic Incident database and the Road Crash Information System Database (RCISD).ResultsSeventy-one SRC involving 62 patients with epilepsy were identified. Thirty-seven SRC resulted in injury and could be identified in the RCISD and compared to NSRC. Seizure-related crashes typically involved a single vehicle (57% vs 29%,p< 0.001) carrying a sole occupant (95% vs 48%,p= 0.001). Most SRC began with an “out of control movement” (51% vs 10%,p< 0.001) and the subsequent collision type differed significantly between the groups (p< 0.001). The majority of SRC were a “collision with a fixed object” (54% vs 17%,p< 0.001) involving an “off path on straight” mechanism (48% vs 10%,p< 0.001). Regarding all 71 SRC, generalized as compared with focal epilepsy crashes involved younger drivers (p< 0.001), seizure-provoking factors (p= 0.033), and occurred earlier in the day (p= 0.004).ConclusionsGiven the distinct SRC features, we propose that clinicians, crash investigators, and driver licensing authorities incorporate collision characteristics into the overall assessment of suspected SRC. Further research should examine restricting driving immediately after risk periods as a harm-minimization strategy.

VCC leverages the underutilized storage and computing resources of vehicles to collaboratively provide traffic management, road safety, and infotainment services to end users, such as drivers and passengers. It is a hybrid technology that improves the resource utilization on vehicles and is able to perform complex computing tasks that cannot be handled by a single vehicle. Despite the appealing advantages, security and privacy threats are severe in VCC due to the sharing of resources among unfamiliar vehicles. In this article, we identify security goals for the interoperability with VCC and provide an AKA framework for VCC. Specifically, we first present the research challenges and open problems for designing a reliable AKA with strong security guarantees for VCC. Then we propose an integrated AKA framework that integrates the single-server 3-factor AKA protocol and the non-interactive identity-based key establishment protocol, and evaluate its performance based on a simulated experimental platform. Finally, several interesting issues are discussed to light up the further research directions on AKA for VCC.

Time for single vehicle combat preparation (TSVCP) is an important characteristic parameter for the operational support feature of armored vehicle. During the development phase, how to validate the TSVCP of armored vehicle through analytic method is a difficult issue in analysis and validation of vehicle supportability. This paper uses Monte Carlo approach and builds a working model for single vehicle combat preparation (SVCP) of armored vehicle, thus realizes the prediction and analysis of the TSVCP of armored vehicle, and finally validates the effectiveness of the approach by example.

This article investigates the temporal stability of the factors that increase the risk of rollover in single-vehicle collisions by utilizing logistic regression modeling to analyze annual records of all single-vehicle collisions that occurred in North Carolina from January 1, 2004 to December 31, 2013. A separate model was developed for each analysis year, and all the models were compared together to identify the most temporally stable factors. The factors that were found to consistently increase the risk of rollover are vehicle age, exceeding the speed limit, rural highways, curved highways, drivers younger than the age of 21 years, drivers influenced by alcohol, illicit drugs, fatigue, or other medical conditions, and when the light-duty vehicle involved in the collision is not a passenger car. Conversely, three factors were found to have no temporally stable effect on the risk of rollover in single-vehicle collisions. Those three factors are undivided highways, driver gender, and adverse enviro...

This paper addresses the longitudinal vehicle behavior before and during lane changes. Hereby, it is desired that the lane-changing vehicle simultaneously follows its predecessors on the lanes before and after the lane change. Specifically, the lane changing vehicle should keep a safe distance to the rearmost predecessor vehicle, while maintaining a small inter-vehicle spacing and supporting driving comfort. To this end, the paper develops an extension of cooperative adaptive cruise control (CACC). Instead of following a single vehicle as in the classical realization of CACC, it is proposed to follow a virtual vehicle that is evaluated based on distance measurements and communicated state information from the predecessor vehicles. A simulation study demonstrates the practicability of the proposed method.

Despite the vast body of literature on vehicle routing problems, little attention has been paid to multi-compartment vehicle routing problems that investigate transportation of different commodities on the same vehicle, but in different compartments. In this project, we present two strategically different versions of the MCVRP in general settings. In the first version, different commodities may be delivered to the customer by different vehicles, but the full amount of each product must be delivered by a single vehicle. In the second version, each customer may only be serviced by a single vehicle, which must deliver the full amount of all commodities demanded by that customer. We present a Branch-and-Price algorithm for solving the two versions of the problem to optimality and we analyze the effect of the strategic decision of whether or not to allow multiple visits to the same customer by comparing the optimal costs of the two versions. Computational results are presented for instances with up to 100 customers and the algorithm can solve instances with up to 50 customers and 4 commodities to optimality.

Motorcycles represent an increasing proportion of traffic fatalities in the United States, accounting for more than 12.7% of the total traffic casualties within 2005–2014. Specifically, in North Carolina, motorcycles comprise less than 1% of vehicles involved in crashes but account for more than 7% of total fatalities, representing a top state in the United States. This study tries to investigate the motorcycle crashes in North Carolina more in depth. In doing so, five years’ (2009–2013) worth of crash data was obtained from the Federal Highway Administration’s Highway Safety Information System database. A partial proportional odds (PPO) logistic regression model was developed to examine the influence of the explanatory variable on the ordered dependent variable, that is, injury severity. Moreover, two other popular ordered-response models, that is, proportional odds and non-proportional odds models, as well as one similar unordered-response model, that is, multinomial logit model, were also deve...

Roundabouts reduce fatal and injury crashes at intersections when converted from other intersection control types. In Wisconsin, roundabouts have been linked to a 38% decrease in fatal and injury crashes. Part of this reduction can be attributed to crash types that result in the mitigation of more serious injuries. However, the reduction comes at a cost because other crash types, such as single-vehicle collisions, may increase. Six years of crash data on 53 roundabouts in Wisconsin were examined for crash causes and geometric characteristics that affected single-vehicle crashes. Weather and impaired driving, particularly by younger drivers, were primary causes for more than half of all single-vehicle crashes at the study roundabouts. Younger drivers (18 to 24 years of age) were involved in a significantly higher proportion of single-vehicle crashes than the total proportion of licensed drivers in that age group. Younger drivers were involved in approximately one-third of all crashes that involved impaired driving and in two-thirds of all speed-related single-vehicle crashes. A negative binomial model was constructed to estimate run-off-road crashes at approaches. It was found that roundabouts with higher approach speeds and higher traffic volumes experienced more run-off-road crashes. Landscaped central islands experienced significantly lower frequencies of run-off-road crashes.

The objective of this research is to identify the factors differentiating between single heavy vehicle collisions at intersections and midblocks by using a binary logit model. Our results show that single vehicle crashes involving heavy vehicle at intersections are more likely to occur on main roads and highways, whereas crashes at midblocks are more likely to occur on divided two-way roads, roads with special facilities or features (e.g. bridge) and roads with a higher percentage of heavy vehicle traffic. Intersection crashes are also more likely to involve vehicles that are turning left or right, resulting in angle crashes and vehicle overturn, whereas midblock crashes are more likely to involve vehicles on higher posted speed roads.

In the Melbourne metropolitan area in Australia, an average of 34 pedestrians were killed every year between 2004 and 2013 in traffic crashes, and vehicle–pedestrian crashes accounted for 24% of all fatal crashes. Mid-block crashes accounted for 46% of the total pedestrian crashes in the Melbourne metropolitan area and 49% of the pedestrian fatalities occurred at mid-blocks. This study developed three models using different decision trees (DTs) to identify the factors contributing to the severity of pedestrian crashes. To improve the accuracy, stability and robustness of the DTs, bagging and boosting techniques were used in this study. The results of this study showed that the boosting technique improved the accuracy of individual DT models by 46%. Moreover, the results of boosting DTs showed that neighbourhood social characteristics were as important as traffic and infrastructure variables in influencing the severity of pedestrian crashes.

In highly interactive driving scenarios, accurate prediction of other road participants is critical for safe and efficient navigation of autonomous cars. Prediction is challenging due to the difficulty in modeling various driving behavior, or learning such a model. The model should be interactive and reflect individual differences. Imitation learning methods, such as parameter sharing generative adversarial imitation learning (PS-GAIL), are able to learn interactive models. However, the learned models average out individual differences. When used to predict trajectories of individual vehicles, these models are biased. This paper introduces an adaptable generative prediction framework (AGen), which performs online adaptation of the offline learned models to recover individual differences for better prediction. In particular, we combine the recursive least square parameter adaptation algorithm (RLS-PAA) with the offline learned model from PS-GAIL. RLS-PAA has analytical solutions and is able to adapt the model for every single vehicle efficiently online. The proposed method is able to reduce the root mean squared prediction error in a 2.5 s time window by 60%, compared with PS-GAIL.

Integrated launch system that consists of aerospace plane (ASP) and another heavy winged vehicle (plane or better ekranoplane) as a booster, are analyzed. Ekranoplane can accelerate ASP to the primary speed of Mach 0.5-0.65 in the required direction that allows to lower the requirements to ASP wing area and its engines power. Some other advantages of the offered integrated transport system are connected with suitable using ekranoplane at ASP landing. Heavy ekranoplane is undoubtedly the single vehicle for realization the progressive idea of docking to descending ASP the addition stage allowing to expand opportunities of its landing. The technology of ASP horizontal landing without undercarriage by docking with ekranoplane at the last stage of decent and the requirements to control systems of both winged vehicles are discussed.

Street Light Energy Saver, a work that focusses on the pressing issue of energy consumption worldwide, is built with the aim of decreasing the power usage of street lamps during the time with no sunlight when their requirement in certain areas is minimum. The aim is improving the efficiency by decreasing the illumination factor of lights. Roads, especially the one connecting to the residencies, have minimum traffic at night hours, and hence, it is crucial to save upon the excess wastage of energy. It is often observed that most of the roads at isolated places are rarely visited after 12 am. The traffic gets filtered past 10 pm, and only few of vehicles pass through those roads. But the system in place is such that whatsoever the traffic maybe or whatsoever is the hour during the night, the street lights are always switched on. With the increasing connectivity of the road and hence the skyrocketing energy consumption, it has become the need of the hour to preserve upon the precious source of energy. This, thus, can only be achieved through some system in hand which, to some extent, walks on the lines of conservation. The Street Light Energy Saver is one such initiative that envisages to achieve the goals. The work when deployed in places will have street lamps emitting 50% of the light when no vehicles are passing. This would be done by deploying IR sensors in order to sense the traffic on the road, during a provided time of the night. The IR sensors will send the signal to the microcontroller which would decide about illuminating the lights to 100%. As soon as a single vehicle would pass, it would switch to 100% and then back to energy saver mode. This would reduce the energy consumptions by great margins.

In this study, a mixed logit model is developed to identify the heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes. The random parameter of the variables in the mixed logit model, the heterogeneous mean, is elaborated by driver gender-based linear regression models. The model is estimated using crash data in New Mexico from 2010 to 2012. The percentage changes of factors' predicted probabilities are calculated in order to better understand the model specifications. Female drivers are found more likely to experience severe or fatal injuries in rollover crashes than male drivers. However, the probability of male drivers being severely injured is higher than female drivers when the road surface is unpaved. Two other factors with fixed parameters are also found to significantly increase driver injury severities, including Wet and Alcohol Influenced. This study provides a better understanding of contributing factors influencing driver injury severities in rollover crashes as well as their heterogeneous impacts in terms of driver gender. Those results are also helpful to develop appropriate countermeasures and policies to reduce driver injury severities in single-vehicle rollover crashes.Copyright © 2016 Elsevier Ltd. All rights reserved. Language: en

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories (No injury, Possible injury, Visible injury, Incapacitating injury and fatality) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes.

In many supply chain scenarios in which short lifespan products are considered, production and transportation decisions must be made in a coordinated manner with no inventory stage. Hence, a solution to this problem conveys information about production starting times of each product lot at facility and delivery times of the lots to various customer-sites located in different geographic regions. In this paper, we study a variant of the problem that single product with limited shelf life is produced at single facility. Once produced, production lot is directly distributed to the customers with non-ignorable transportation time by single vehicle having limited capacity before the lifespan. Objective is to determine the minimum time required to produce and deliver all customer demands. To this end, we develop a branch-and-cut (B&C) algorithm using several valid inequalities adopted from the existing literature to improve lower bounds and applying a local search based on simulated annealing approach to improve...

Run-off-road (ROR) crashes account for a large proportion of fatalities and serious injuries to vehicle occupants, especially in rural areas. While performing crash severity analysis using discrete choice models (DCMs), researchers may be confused by the following questions: first, should an ordered or unordered model structure be used and secondly, which modeling level is more appropriate, basic or advanced? A model selection framework is developed considering the following factors: (1) model structure — ordered or unordered; (2) intrinsic deficiency of each model; (3) computational burdens; (4) complexity of parameter interpretation; and (5) model fitness. Historical ROR crash data were utilized to illustrate how to choose an appropriate DCM based on the proposed framework. Using statistical tests and comparison of evaluation and validation measurements, both the mixed logit model and the partial proportional odds model yield a reasonable performance. All factors that significantly affect the severity level of a single-vehicle ROR crash were identified as well.

The large-scale of unmanned aerial vehicle applications has escalated significantly within the last few years, and the current research is slowly hinting at a move from single vehicle applications to multivehicle systems. As the number of agents operating in the same environment grows, conflict detection and resolution becomes one of the most important factors of the autonomous system to ensure the vehicles’ safety throughout the completion of their missions. The work presented in this paper describes the implementation of the novel distributed reactive collision avoidance algorithm proposed in the literature, improved to fit a swarm of quadrotor helicopters. The original method has been extended to function in dense and crowded environments with relevant spatial obstacle constraints and deconfliction manoeuvres for high number of vehicles. Additionally, the collision avoidance is modified to work in conjunction with a dynamic close formation flight scheme. The solution presented to the conflict detection and Resolution problem is reactive and distributed, making it well suited for real-time applications. The final avoidance algorithm is tested on a series of crowded scenarios to test its performances in close quarters.

The subject of this paper is the coordination of lane changes of autonomous vehicles on a two-lane road segment before reaching a given critical position. We first develop an algorithm that performs a lane change of a single vehicle in the shortest possible time. This algorithm is then applied iteratively in order to handle all lane changes required on the considered road segment while guaranteeing traffic safety. Various example scenarios illustrate the functionality of our algorithm.

Objectives: To quantify the odds of fatal injuries associated with drivers involved in single-vehicle, run-off-road (SVROR), injury crashes. Methods: An in-service safety evaluation was carried out using multivariate logistic regression models. Results: The odds of motorist death was lower for w-beam guardrail crashes as compared to tree, pole, and concrete barrier crashes. On the other hand, there was no statistically significant difference between the odds of motorist death in concrete barrier crashes as compared to tree or pole crashes. The odds of motorist death were lower for curbs and collision-free crashes as compared to tree, pole, and barrier crashes. Thus, obstacles should be removed whenever possible and barriers installed only whenever absolutely necessary. The lack of vehicle containment (in barrier crashes) was found: (i) to tend to occur on higher-posted-speed-limit roads and result in a higher percentage of fatal crashes, (ii) to be more prevalent with the less rigid barrier type, and (iii) to result in a consistently higher percentage of fatal crashes under the concrete barrier category. Conclusions: Findings not only support state-of-the-art roadside design guidelines and crash-testing criteria, but they may also be useful in evaluating proposed roadside safety improvements.

In the process of image acquisition monitoring in vehicle license plate recognition system, the accuracy of license plate recognition will decrease which is caused by the complicated application scenarios, poor performance of the equipment and system, and the degeneration of surveillance images. In order to overcome the shortcoming, a super-resolution restoration method for single vehicle image based on efficient sub-pixel convolutional neural network (ESPCN) model is proposed (hereinafter referred to as ESPCN-VISR method). In the experiments, ESPCN-VISR method was proved to be superior by four indexes of quantitative assessment which are peak signal to noise ratio (PSNR), structural similarity (SSIM), vehicle license plate recognition accuracy (VLPRA), and calculation time of reconstruction (CTR). Compared with the methods based on sparse dictionary learning and deep convolutional neural network SRCNN, the ESPCN-VISR method can improve the performance of vehicle license plate recognition system with vehicle license plate sample image dataset LPI-1000.

To understand the injury severity of bicyclists and motorcyclists in single vehicle collisions, we performed a retrospective analysis of forensic autopsy cases of 25 motorcyclists and 14 bicyclists performed from 1999 to 2018. Collision details, injury characteristics, and involvement of alcohol were examined. The injury severity between bicyclists and motorcyclists was compared. The average age of victims was 62.3 years. All motorcyclists and no bicyclists wore a helmet. Motorcyclists had more severe injuries than bicyclists (mean Injury Severity Score of 42 and 27, respectively). The motorcyclists had more severe chest injuries but fewer severe facial injuries than bicyclists, owing to the difference in collision velocity or rate of helmet use (p 0.05). Alcohol was present in the blood of 52.0% of bicyclists but no motorcyclists. The mean blood alcohol concentration of these bicyclists was 1.59 mg/mL. The bicyclists under the influence of alcohol had more severe injuries to neck and upper extremities than non-drunken bicyclists (p 0.05). Our results may be useful for determining the cause of death and reconstructing the mechanisms of fatal injuries in bicyclists and motorcyclists.

This paper addresses an integrated P-D scheduling model arising in a three-stage supply chain, which involves three stages: a single vehicle after processing by different suppliers, manufacturers, distributors and consumers located in separate geographic regions. The contribution of this paper is to provide an integrated P-D scheduling under fixed departure times in supply chain with consideration of transportation fleet, integration of supplier-manufacturer. The mathematical model was proposed as a mixed integer programming and solved by CPLEX software in small-scale problems to validate the model. Given the complexity of the solution and NP-hard structure of the proposed problem, genetic algorithm was used to find quality solutions by MATLAB software. Finally, the results of research show that managers of the case study can deliver orders to customer in minimum time and cost. According to the results, the proposed algorithm (GA) has a desired level of quality in solving the P-D real problems.

Improving air quality in urban environments and transportation systems is crucial. Concerns are related to health and environmental issues associated with huge costs. Car cabin is a microenvironment where pollutants can accumulate with possible risks for occupants. In automotive engineering, it has then become mandatory to study the path and dispersion of such pollutants emitted from the tailpipe of a car. In the present paper, the relation between the flow topology and the dispersion of ultrafine particles (UFP) in the wake of a vehicle is discussed. Experiments were undertaken at a reduced scale using simplified car models. Experimental conditions were defined to be representative of a vehicle in an urban environment. Based on experimental data, a simplified analytical model is developed, which aims at describing the concentration fields of UFP in the wake of a single vehicle for different rear slant angles. The strengths and limits of the present model are discussed and ways of improvements are suggested. Additional experiments are presented to assess the influence of the inter-vehicle distance on this recirculation region. Critical inter-vehicle distances were determined based on defined criteria for different rear slant angles of the leading vehicle and compared to safety clearances.; Il est essentiel d'améliorer la qualité de l'air dans les milieux urbains et les systèmes de transport. Les préoccupations sont liées aux questions de santé et d'environnement associées à des coûts énormes. L'habitacle d'une voiture est un micro-environnement où les polluants peuvent s'accumuler avec des risques possibles pour les occupants. Dans l'industrie automobile l'ingénierie, il est alors devenu obligatoire d'étudier le cheminement et la dispersion de ces polluants émis par le tuyau d'échappement d'une voiture. Dans le présent article, la relation entre la topologie de l'écoulement et la dispersion des particules ultrafines (UFP) dans le sillage d'un véhicule est discutée. Les expériences ont été faites à une échelle réduite en utilisant des modèles de voitures simplifiés. Les conditions expérimentales ont été définies pour être représentatif d'un véhicule dans un environnement urbain. Sur la base de données expérimentales, un modèle analytique est développé, qui vise à décrire les champs de concentration de l'UFP dans le sillage d'un seul véhicule pour des angles d'inclinaison arrière différents. Les points forts et les limites du présent modèle sont et des moyens d'amélioration sont suggérés. Des expériences supplémentaires sont présentées pour évaluer l'influence de la distance inter-véhicules sur cette région de recirculation. Distances inter-véhicules critiques ont été déterminés en fonction de critères définis pour les angles d'inclinaison arrière du véhicule de tête et par rapport aux autorisations de sécurité.

Single-vehicle (SV) crashes are of major concerns because of their high fatality rates. To understand the proneness of high injury severity for vehicle operators brought about by SV crashes without the confounding influence of other road users, this study focuses on those SV crashes without colliding with pedestrians, which are defined as out-of-control SV crashes given the general consequence of involved vehicles. Moreover, to compare the influence of contributory factors (including driver-vehicle/rider-vehicle, roadway, and environmental characteristics) by vehicle types, the injury severity for riders of motorized two-wheelers and drivers of other motorized vehicles are investigated separately using two disaggregated ordered probit models. The results show that for both riders and drivers, variables such as age (65 and above), drink driving, error type of failing to have proper control, driving maneuvers of left and right turns as well as driving after midnight are associated with more severe injuries whereas factors such as wet, oily or sandy surfaces are related to less severe injury. Four other variables, i.e., foreign vehicle registration, probation or expired license, high speed-limit roads, and type of median lane, have different influences on riders and drivers on injury severity. Additionally, factors such as road traffic type and nationality are only found to significantly influence only riders and drivers respectively. The results shed light on both the similar and different causes of high injury severity for riders and drivers involved in out-of-control SV crashes. Based on the findings, targeted countermeasures may be introduced from multiple perspectives such as driver education and policy development to improve non-traffic-interactive safety.

Currently, many automated vehicle systems primarily perceive the environment from a single perspective and as a result are unable to leverage additional scene information from the viewpoint of other vehicles on the road using vehicleto-vehicle communication technologies. We study how increased data sharing can improve the perception capabilities of automated vehicles. Our methodology shares sensor measurements and objects detected by state-of-the-art deep learning networks between vehicles to increase the automated driving systems confidence of detecting objects using a 3D sensor fusion algorithm. This approach can benefit scenarios where an object may be occluded (fully or partially) or located too far away to classify accurately by a single vehicle alone.