Highway Vehicle Research Articles

Coordinated Planning of EV Charging Stations and Mobile Energy Storage Vehicles in Highways With Traffic Flow Modeling

Coordinated Planning of EV Charging Stations and Mobile Energy Storage Vehicles in Highways With Traffic Flow Modeling

Research and Design of an Optimal Management Strategy for Simultaneous Wireless Power and Information Transmission for Highway Vehicles

Research and Design of an Optimal Management Strategy for Simultaneous Wireless Power and Information Transmission for Highway Vehicles

Multi-Sensor Fusion-Based Highway Vehicle Behavior Prediction and Its Application in Adaptive Cruise Control

Multi-Sensor Fusion-Based Highway Vehicle Behavior Prediction and Its Application in Adaptive Cruise Control

Real-time vehicle detection system on the highway

Locating and classifying different types of vehicles is a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification, with deep learning models now dominating the field of vehicle detection. However, vehicle detection in Bangladesh remains a relatively unexplored research lacuna. One of the main goals of vehicle detection is its real-time application, with “You Only Look Once” (YOLO) models proving to be the most effective. This paper compared real-time vehicle highway detection systems using YOLOv4, Faster R-CNN and SSD algorithms to determine the best performance. A vehicle detection and tracking system was also developed that improved highway safety. Vehicle trials compared the real-time performances of the YOLO, Faster R-CNN and SSD algorithms in detecting and tracking highway vehicles by measuring precision, recall, F1-score and operating speed. Models for each algorithm were constructed and each model was trained and tested, with performance measured using a confusion matrix. This statistical tool assessed the efficiency of the system using a prepared test dataset and evaluated the results using appropriate indicators such as real-time road lines, traffic signs and vehicle detection false positive rates. Results showed that the YOLOv4 algorithm outperformed Faster R-CNN and SSD in real-time vehicle detection and tracking on highways. YOLOv4 also processed the results more quickly and proved superior in detecting and tracking objects in real time. The Faster R-CNN algorithm gave high object detection, tracking accuracy and recall while reducing the number of locations needing detection, with the SSD algorithm providing high precision, recall and good image detection results.

Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations

Safe railway operation is vital for public safety, the environment, and property. Concurrent with climbing amounts of rail traffic on the Canadian rail network are increases in the last decade in the annual crash counts for derailment, collision, and highway railroad grade crossings (HRGCs). HRGCs are important spatial areas of the rail network, and the development of community areas near railway tracks increases the risk of HRGC crashes between highway vehicles and moving trains, resulting in consequences varying from property damage to injuries and fatalities. This research aims to identify major factors that cause HRGC crashes and affect the severity of associated casualties. Using these causal factors and ensemble algorithms, machine learning models were developed to analyze HRGC crashes and the severity of associated casualties between 2001 and 2022 in Canada. Furthermore, spatial autocorrelation and optimized hotspot analysis tools from ArcGIS software were used to identify hotspot locations of HRGC crashes. The optimized hotspot analysis shows the clustering of HRGC crashes around major Canadian cities. The analysis of cluster characteristics supports the results obtained for causal factors of HRGC crashes. These research outcomes help one to better understand the major causal factors and hotspot locations of HRGC crashes and assist authorities in implementing countermeasures to improve the safety of HRGCs across the rail network.

Real-road NOx and CO2 emissions of city and highway China-6 heavy-duty diesel vehicles

The emission of heavy-duty vehicles has raised great concerns worldwide. The complex working and loading conditions, which may differ a lot from PEMS tests, raised new challenges to the supervision and control of emissions, especially during real-world applications. On-board diagnostics (OBD) technology with data exchange enabled and strengthened the monitoring of emissions from a large number of heavy-duty diesel vehicles. This paper presents an analysis of the OBD data collected from more than 800 city and highway heavy-duty vehicles in China using remote OBD data terminals. Real-world NOx and CO2 emissions of China-6 heavy-duty vehicles have been examined. The results showed that city heavy-duty vehicles had higher NOx emission levels, which was mostly due to longer time of low SCR temperatures below 180°C. The application of novel methods based on 3B-MAW also found that heavy-duty diesel vehicles tended to have high NOx emissions at idle. Also, little difference had been found in work-based CO2 emissions, and this may be due to no major difference were found in occupancies of hot running.

Assessment of noise and vibration characteristics of Botryococcus braunii microalgae and hydrogen blends in internal combustion engines for highway vehicles

Extensive study has revealed biodiesel as a promising fuel obtained from a variety of sources, with its availability being a significant benefit. While biodiesel's performance and emission characteristics have been widely researched and proved suitable as a fuel, the most important aspects of noise and vibration characteristics in highway vehicles is remain unknown. In this study the effect of Botryococcus braunii microalgae on the four stoke diesel engine has been studied under the highway vehicle loading conditons. The biodiesel blends used in this study were B10% (10% of Botryococcus braunii biodiesel + 90% of pure diesel) and B20% (20% of Botryococcus braunii biodiesel + 80% of pure diesel). In addition to the biodiesel blends and neat diesel, the hydrogen was also supplied at the flow rate of 5 lpm. In order to avoid the backfire, flashback arrestor had been connected in the fuel inline manifold. The results obtained by using pure diesel were taken as reference and the biodiesel blend results were compared with them. A series of test conducted on the blends at five different loads of 0%, 25%, 50%, 75% and 100%. Sound pressure sensor and accelerometer were used to measure noise and vibration with respect to engine loads. Addition of hydrogen reduced the mass fraction in all blends, regardless of the fuel type. Hydrogen aids the combustion process thus there is an significant reduction in both highway vehicles engine noise and vibration has been noted irrespective of operating conditions. From the results, it is clear that the addition of hydrogen in both biodiesel and diesel showed a significant reduction in sound and acceleration. This finding supports the promising potential of microalgae blends as viable alternatives for highway vehicles.

Chlorella vulgaris microalgae derived blends in micro gas turbine engines: A comprehensive environmental impact analysis for highway vehicle applications

The significant benefits of microalgae biofuel have made it a preferred choice in various industries, including aviation. Microgas turbine engines can be promising choice for highway vehicles. This research paper investigates the process of producing fuel from microalgae, as well as its performance and emission characteristics in the micro gas turbine engine operating at sea level conditions for highway vehicle applications. A two-step extraction and transesterification process were employed to convert the biofuel, using a batch stirrer reactor for esterification. The preparation of biofuel blends followed the volume fraction method at the concentration of 10% and 30%. Additionally, nanoparticles were incorporated into the blends at a 50 ppm concentration to enhance overall characteristics of the engine. Titanium dioxide nanoparticles, known for their high surface-to-volume ratio, were used for this purpose. Experimental tests were conducted on a 30 kW micro gas turbine engine to evaluate the fuel's quality in terms of performance and emissions. Various parameters were measured at different engine speed, including 30,000 rpm, 40,000 rpm, 50,000 rpm, 60,000 rpm, and 70,000 rpm. The results obtained clearly demonstrate that the optimal blending of biofuel with neat Jet-A fuel improves performance by generating the required thrust while significantly reducing harmful gas emissions. Furthermore, the addition of nanoparticles acted as a catalyst and enhanced the quality of the fuel blends, making them an efficient alternative for aircraft engines as well. Added to above, there is an significant reduction in the emission of the CO has been noted across wide range of speed. From the results it is evident that microgas turbine engine can be an ideal choice for the highway vehicles to meet reduced carbon emissions.

Linear and landscape disturbances alter Mojave desert tortoise movement behavior

IntroductionAnimal movements are influenced by landscape features; disturbances to the landscape can alter movements, dispersal, and ultimately connectivity among populations. Faster or longer movements adjacent to a localized disturbance or within disturbed areas could indicate reduced habitat quality whereas slower or shorter movements and reduced movements may indicate greater availability of resources. The Mojave desert tortoise (Gopherus agassizii) is a threatened species that is challenged by anthropogenic disturbances.MethodsWe studied tortoise movements using Global Positioning System (GPS) loggers at multiple sites in the Mojave Desert of Nevada and California. Tortoises at our sites encountered localized, linear human infrastructure, including paved roads, dirt roads, and fences, as well as landscape-scale disturbances [wildfire, off highway vehicle use (OHV), livestock grazing area]. We fit two-state (moving and encamped) Hidden Markov models to GPS logger data to infer how tortoise movement behavior relates to anthropogenic and natural features.ResultsWe found that temporal covariates, individual-level random effects (intercepts), and sex best explained state transition probability in all sites. We compared relationships between tortoise movement and linear disturbances, which varied depending on site and context. Tortoises made longer movements within the OHV recreation area, near most dirt roads, and near a low-traffic paved road, indicating that tortoises avoid these habitat disturbances. Conversely, tortoises made shorter movements in areas of higher slope and near highways, suggesting that these features may restrict movement or provide resources that result in prolonged use (e.g., forage or drinking locations). Tortoises that encountered fences around utility-scale solar installations were more active and made longer movements near fences, indicative of pacing behavior.DiscussionThese results provide insight into how different disturbances alter tortoise movement behavior and modify tortoise habitat use, providing information that can be used to manage tortoise habitat.

Enhanced Exhaust after-Treatment Warmup in a Heavy-Duty Diesel Engine System via Miller Cycle and Delayed Exhaust Valve Opening

The exhaust after-treatment (EAT) threshold temperature is a significant concern for highway vehicles to meet the strict emission norms. Particularly at cold engine start and low loads, EAT needs to be improved above 250 °C to reduce the tailpipe emission rates. Conventional strategies such as electrical heating, exhaust throttling, or late fuel injection mostly need a high fuel penalty for fast EAT warmup. The objective of this work is to demonstrate using a numerical model that a combination of the Miller cycle and delayed exhaust valve opening (DEVO) can improve the tradeoff between EAT warmup and fuel consumption penalty. A relatively low-load working condition (1200 RPM speed and 2.5 bar BMEP) is maintained in the diesel engine model. The Miller cycle via retarded intake valve closure (RIVC) is noticeably effective in increasing exhaust temperature (as high as 55 °C). However, it also dramatically reduces the exhaust flow rate (over 30%) and, thus, is ineffective for rapid EAT warmup. DEVO has the potential to enhance EAT warmup via increased exhaust temperature and increased exhaust flow rate. However, it considerably decreases the brake thermal efficiency (BTE)—by up to 5%—due to high pumping loss in the system. The RIVC + DEVO combined technique can elevate the exhaust temperature above 250 °C with improved fuel consumption—up to 10%—compared to DEVO alone as it requires a relatively lower rise in pumping loss. The combined method is also superior to RIVC alone. Unlike RIVC alone, the RIVC + DEVO combined mode does not need the extreme use of RIVC to increase engine-out temperature above 250 °C and, thus, provides relatively higher heat transfer rates (up to 103%) to the EAT system through a higher exhaust flow rate. The RIVC + DEVO combined method can be technically more difficult to implement compared to other methods. However, it has the potential to maintain accelerated EAT warmup with improved BTE and, thus, can keep emission rates at low levels during cold start and low loads.

0266 An mHealth intervention to deliver personalized messages to increase adherence to OSA care among transportation workers

Abstract Introduction Transportation workers are at risk for obstructive sleep apnea (OSA), yet OSA is commonly undiagnosed in this population. In a two-phase study, we design and evaluate a tailored, mobile health (mHealth) intervention aimed at achieving OSA awareness, including changes in OSA beliefs, among operators of large highway maintenance vehicles. Methods In Phase 1, we recruited highway maintenance vehicle operators employed at worksites in the US northeast for focus groups to examine OSA beliefs and barriers. Results of the Phase 1 focus groups were used to inform the tailored intervention. In Phase 2, we evaluated the tailored approach, “Sleep Strong, Live Long,” in a mini-RCT. Worksites were randomized to intervention (Sleep Strong, Live Long) or control. Employees at both worksites completed baseline questionnaires and follow-up questionnaires 2-months post-baseline. Employees at the intervention worksite received a 30 minute in-person education session, access to an interactive website including video success stories, then follow-up text messages over 2 months as part of the Sleep Strong, Live Long program. The control condition received the intervention after completing follow-up surveys. We compared responses on the follow-up surveys between intervention and control conditions using linear regression with baseline responses as covariates. Results Phase 1 themes included common sleep complaints, inconsistent work schedules, and misconceptions about OSA (n=45). 75% of Phase 1 participants reported resistance to OSA behavior change. Phase 2 mini-RCT participants (n= 38) were on average 43.4 (sd=12.1) years old. Participants were 86.8% male and 67.6% reported an annual household income of $50,000 or more. Exposure to the intervention was associated with an improvement in OSA literacy (b=0.14, p=0.037), but not OSA self-efficacy (b=0.12, p=0.330) or OSA readiness to change (b=0.76, p=0.336). Conclusion We provide preliminary support for improving OSA literacy among a sample of transportation workers. Future research may evaluate the tailored program in a larger sample of workers and with a longer follow-up period to consider whether OSA literacy and self-efficacy mediate actual OSA behaviors, such as changes in OSA evaluation and treatment. Support (if any) This work was supported by the National Heart, Lung, and Blood Institute (NHLBI) at the National Institutes of Health (NIH, Grant # K01HL150339).

Dynamic response of a multi-span suspension bridge subjected to highway vehicle loading

Dynamic response of a multi-span suspension bridge subjected to highway vehicle loading

Dynamic wireless charging lane reversal for connected and automated electric vehicles in highway

Wireless charging lanes enable electric vehicles to be charged while driving, promoting the electrification and sustainability of transport systems. However, they cannot be fully covered on the highway due to construction costs. Conversion of several lanes of highways to wireless charging lanes is a more feasible option. In this paper, we propose a dynamic reversal strategy for wireless charging lanes in the presence of connected and automated vehicles on a highway. The proposed strategy aims to improve highway traffic efficiency while meeting electric vehicles’ uneven charging demand in different directions. A centralized controller is set to change wireless charging lane directions and guide traffic flow dynamically. Electric vehicles can access real-time lane reversal information for lane-changing and speed adjustment behaviors with connected and automated vehicle technology. We propose a modified multi-lane cell transmission model to model the traffic flow with different speed preferences on wireless charging and regular lanes. A mixed-integer linear program is developed for the proposed lane reversal strategy. Numerical experiments are conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed strategy can effectively reduce road congestion, meet the dynamic charging demand, and reduce the energy consumption of electric vehicles in different directions.

The clean-energy challenge

The Issues and Events item “Electrification of cars and trucks likely won’t disrupt the grid” (Physics Today, April 2022, page 22) by David Kramer is timely and accurate as far as it goes. But by omitting mention of nonhighway transportation, and the rest of the economy for that matter, it unintentionally makes the growth in electricity usage on the path to decarbonization of our economy seem to be nearly business as usual. Several recent studies, however, show that a carbon-neutral US economy in 2050 will require around four times as much electricity as we use today.1–31. E. Larson et al., Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Princeton U. (29 October 2021).2. M. Ram et al., Global Energy System Based on 100% Renewable Energy: Power, Heat, Transport and Desalination Sectors, Lappeenranta U. Technology and Energy Watch Group (March 2019).3. J. H. Williams et al., AGU Adv. 2, e2020AV000284 (2021). https://doi.org/10.1029/2020AV000284Synthesis of chemical fuel for aviation, military, and nonhighway vehicles will require more electricity than the electrification of highway vehicles discussed in Kramer’s story. The electrification of homes, businesses, and industry—including synthesis of hydrocarbon feedstocks—will require twice as much again. That prodigious increase in generation, transmission, and, hopefully, storage is most certainly different from what was needed to support the introduction of air conditioning.The new electric grid will be much larger and will operate very differently from the old one. Power will be transmitted longer distances from regions where sunlight and wind are abundant. Roughly half that power will be for battery charging and a massive new electrochemical industry, both amenable to load management to match the remaining intermittence of renewable power supply. The tendency to treat decarbonization of economic sectors in isolation and thereby miss the big picture is in part a reflection of the incremental approach in our policy. To plan and finance the greatest industrial build-out since the railroad boom of the late 19th century, we must have a comprehensive policy that sets clear goals and brings long-term investor confidence. And we need it soon. We only have 30 years to complete the project!ReferencesSection:ChooseTop of pageReferences <<1. E. Larson et al., Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Princeton U. (29 October 2021). Google Scholar2. M. Ram et al., Global Energy System Based on 100% Renewable Energy: Power, Heat, Transport and Desalination Sectors, Lappeenranta U. Technology and Energy Watch Group (March 2019). Google Scholar3. J. H. Williams et al., AGU Adv. 2, e2020AV000284 (2021). https://doi.org/10.1029/2020AV000284, Google ScholarCrossref© 2023 American Institute of Physics.

Large eddy simulations of the turbulent channel flow over dimpled surfaces

Reducing skin friction has a key role in the efficiency of rail, highway, and airway transport vehicles or naval systems such as ships and underwater vehicles. In recent years, there is a growing interest in investigating turbulent drag-reducing capabilities of dimpled surfaces, which have great potential as a passive solution, while there still exists highly conflicting views and drag reduction rates reported in the literature as well as a lack of information about the drag reduction mechanism. In this study, large-eddy simulations (LES) were performed to investigate the characteristics and physical mechanism of the fluid flow over dimpled surfaces in a fully developed channel flow. The Reynolds number based on the channel height and the mean bulk velocity was nearly 5600 for all cases examined. Within the framework of the study, various dimple depth to diameter ratios as well as different dimple arrangements and geometries were considered. The detailed mean and instantaneous flow fields, turbulent kinetic energy budget and spectral characteristics of the flow are presented. The study revealed the potential of the dimpled surface in reducing skin friction and provided critical information about the flow features affecting the performance of the dimples.

Study on Braking Safety and Reliability of Highway Vehicles under a Heavy Rainfall Environment Based on Stopping Sight Distance

Study on Braking Safety and Reliability of Highway Vehicles under a Heavy Rainfall Environment Based on Stopping Sight Distance

Static response of a multi-span suspension bridge subjected to highway vehicle loading

Static response of a multi-span suspension bridge subjected to highway vehicle loading

Leveraging connected vehicle platooning technology to improve the efficiency and effectiveness of train fleeting under moving blocks

This paper leverages emerging highway vehicle platooning technology to improve the efficiency and effectiveness of fleeting trains at minimum headways under moving blocks. The research aims to better understand how closely following trains respond to different throttle and brake control algorithms, and, using insights gained from automobile and truck platooning technology, develop improved train control algorithms balancing fuel efficiency and train headway. To do so, a detailed multi-train performance simulator is developed to evaluate following train control algorithms and then adapt highway vehicle platooning control methods to the heavy haul freight rail domain. Five following train control algorithms under two different communication topologies are formulated to more intelligently consider information on the status of the train ahead when specifying throttle or brake settings for each following train. With string stability, following trains attenuate the actions of preceding trains and each successive train requires less aggressive acceleration and braking rates to maintain headways. The simulation results suggest that certain families of control laws are better than others at managing train separation and fuel consumption within train fleets. The results of this research will allow industry practitioners to develop improved locomotive driver advisory and semi-autonomous adaptive train cruise control systems for the operation of fleets of trains under moving blocks, and railroad operators to make more informed decisions regarding the potential fuel efficiency and capacity benefits of these systems.

Collaborative Decision-Making Method of Emergency Response for Highway Incidents

With the continuous increase in highway mileage and vehicles in China, highway accidents are also increasing year by year. However, the on-site disposal procedures of highway accidents are complex, which makes it difficult for the emergency department to fully observe the accident scene, resulting in the lack of sufficient communication and cooperation between multiple emergency departments, making the rescue efficiency low and wasting valuable rescue time, and causing unnecessary injury or loss of life due to the lack of timely assistance. Thus, this paper proposes a multi-agent-based collaborative emergency-decision-making algorithm for traffic accident on-site disposal. Firstly, based on the analysis and abstraction of highway surveillance videos obtained from the Shaanxi Provincial Highway Administration, this paper constructs an emergency disposal model based on Petri net to simulate the emergency on-site disposal procedures. After transforming the emergency disposal model into a Markov game model and applying it to the multi-agent deep deterministic strategy gradient (MADDPG) algorithm proposed in this paper, the multiple agents can optimize the emergency-decision-making and on-site disposal procedures through interactive learning with the environment. Finally, the proposed algorithm is compared with the typical algorithm and the actual processing procedure in the simulation experiment of an actual Shaanxi highway traffic accident. The results show that the proposed emergency-decision-making method could greatly improve collaboration efficiency among emergency departments and effectively reduce emergency response time. This algorithm is not only superior to other decision-making algorithms such as genetic algorithm (EA), evolutionary strategy (ES), and deep Q network (DQN), but also reduces the disposal processes by 28%, 28%, and 42%, respectively, compared with the actual disposal process in three emergency disposal cases. In summary, with the continuous development of information technology and highway management systems, the multi-agent-based collaborative emergency-decision-making algorithm will contribute to the actual emergency response process and emergency disposal in the future, improving rescue efficiency and ensuring the safety of individuals.

Deep learning based highway vehicles detection and counting system using computer vision

In the field of highway management, intelligent vehicle detection and counting are becoming increasingly significant. City planners have been fighting the issue of traffic analysis for years. To analyze traffic and streamline the process, intelligent methods are being created. The type of vehicles and the number of vehicles in an area over some arbitrary time period may be taken into account. Over the years, humans have created a variety of mechanisms, because the vehicles are come in different sizes, it is still difficult to identify them, which can directly impact the accuracy of vehicles count. In the field of vision research, computer vision is one of the most often used applications for traffic scenarios. A variety of computer vision applications, including object detection and categorization, have proven that deep learning techniques like Convolutional Neural Networks (CNNs) greatly outperform conventional approaches. Hence in this work, deep learning-based highway vehicles detection and count system suing computer vision is presented. Vehicles on the highway can be efficiently detected by this technique. YOLOv3 (You Look Only Once Version 3) is a real-time object detection technique which is used to detect vehicles in real-time. Precision, recall and real-time rate are measured to evaluate the performance of this system.