How urban vehicle composition influences population concentrations in Indian cities?: a panel data analysis

This paper proposes an electric vehicle charging load forecasting method that considers the impact of different types of vehicles. First, the Bass model is used to predict the number of electric vehicles, and the model is verified by predicting and analyzing the number of electric vehicles. It also uses Monte Carlo simulation method to calculate the charging load of electric vehicles. For different types of electric vehicles, the charging characteristics are analyzed by considering the charging period, start time, daily mileage, charging mode, and charging power. Perform superposition to obtain the total charging load curve, and complete the charging load forecast of electric vehicles.

Characterization, concentrations and emission rates of polycyclic aromatic hydrocarbons in the exhaust emissions from in-service vehicles in Damascus

The objective of this study is to find factors influencing the injury severity of vehicle at-fault accidents in Shenyang (China), and discuss the commonalities and differences between passenger and freight vehicle accidents. We analyzed 1647 traffic accidents from 2015 to 2017, in which motor vehicles were fully or mainly responsible, including 1164 traffic accidents caused by passenger vehicles and 483 traffic accidents caused by freight vehicles. Twenty influencing factors from the aspects of accident, driver, time, space and environmental attributes are analyzed to find their statistical connection with injury severity using the binary logistic regression model. For passenger vehicles, five influencing factors (side collision; illegal act while driving; hit-and-run; season and administrative division), showed statistically significant correlations with the injury severity. For freight vehicles, three influencing factors (illegal act while driving; season and administrative division), showed statistically significant correlations with the injury severity. Illegal act while driving is the only common influencing factor for the injury severity of both passenger and freight vehicle accidents. Side collision and hit-and-run are significant influencing factors for the injury severity of passenger vehicle accidents, but not for freight vehicle accidents. Season and administrative division present different results on influencing passenger and freight vehicle accidents. Based on these results, measures including driver education and road infrastructure improvement could be implemented to reduce the injury severity of accidents in passenger and freight vehicles.

The continuously growing transportation sector has become the second largest, yet increasing, industrial emissions source of CO2, posing serious challenges to global environmental security. Among the various transport modes, road transportation yields the highest cumulative level of CO2 emissions. However, these emissions have not been sufficiently investigated in previous studies, especially with respect to analyses from the perspective of vehicle emission sources. This can make source management and emissions reduction difficult. To address these methodological issues, this study aims to build a highway traffic carbon emissions monitoring and spatial analysis system, employing the mobile carbon sources concept, and establish a carbon emissions model encompassing all types of passenger and freight vehicles based on interstation O-D traffic flow data recorded by the toll collection network, to calculate vehicle carbon emissions and create a mobile carbon source emissions map. Empirical analyses in Guangdong Province revealed that, compared with conventional studies, the mobile carbon source emission mapping approach can accurately identify vehicle types with higher emissions while assisting with source management. Of the average total daily carbon emissions from all types of vehicles that use highways (15,311 t), 57.10% originated from freight vehicles (8,743 t) while passenger vehicles contributed 42.90%. By specific vehicle type, emissions mainly originated from small and medium-sized vehicles, including Class I passenger vehicles (i.e., cars) and Class I and III freight vehicles. Further, the proposed method could locate road sections characterized by high carbon emissions. High-emission sections in Guangdong Province were mainly spatially autocorrelated, with peak aggregations on national highways; near economically developed and densely populated areas; and adjacent to surrounding airports, ports, and overpass roads. This study improves the scientific and spatial analytical accuracy for carbon emissions measurements of highway vehicles, thus informing source management and sustainable development, as well as providing technical support for attaining carbon neutrality in China.

Highly increasing vehicle population results in transportation problems, and thus it leads to the requirement of more efficient transportation systems. Indian urban road traffic consists of different types of vehicles with different sizes, speed, and maneuverability moving on the same right of way without lane discipline and without obeying general traffic rules. They create chaos on junctions and result in enormous delays, more fuel consumption, air and noise pollution, accidents, and also restrict the movement of emergency vehicles. Therefore, it is necessary to quantify the travel time delay in existing traffic conditions. The aim of this study is to quantify the delay in travel time and to identify its causes. As per that the various traffic surveys have been carried out on selected stretches of Kalupur region in Ahmedabad. In this study, it is proposed to measure the travel time delay and to ascertain the notable factors which result in these delays. License plate method and GPS in floating car method are used to collect travel time data during peak hours in the morning and evening, respectively. With the help of the data collected, Flow–Delay model is generated. Average travel time observed in this area is about 15–20 min/km during evening peak hours. It is found that the travel time delay depends on traffic flow, its composition, slow-moving vehicles, haphazard movement of vehicle without lane discipline, boarding alighting of passengers on the bus stops, and rickshaw stops. It is suggested to provide an efficient mass transport system at separate levels in this area.

The registration and analysis of seismic signals generated by different types of machines and vehicles are areas that, thanks to modern technological capabilities, create an opportunity to study various aspects of environmental behavior and generate timedepending loading function for civil engineering analysis. These analyses can be used in various areas of engineering research - on the one hand as a means of identifying the actuator (vehicle) and assessing its operating mode, and on the other hand - as a means of modelling the impact of generated mechanical waves on neighbouring objects. The application of this type of research can be clearly oriented towards assessing the impact of urban noise (traffic) on the buildings and facilities. With this assessment, technical solutions can be developed to reduce the harmful effects of traffic and machine vibrations. In the present study, data from accelerometers were used to determine the parameters of oscillation caused by different types of vehicles. Through the measurements and analysis, an opportunity was sought to identify the vehicles by means of FFT analysis and response spectra. Different types of accelerometers, from piezoelectric to molecular, were used. The results were subjected to mathematical transformations with SeismoMatch, V. 2016 (Spectral Analysis) and Excel (FFT analysis) and on this basis different markers were identified in the form of accelerations, velocities, and displacements in the registration positions.

Critical gap is a crucial factor for capacity estimation and safety evaluation at uncontrolled mid-block median openings (MBMOs). The U-turning vehicle makes a U-turn when a sufficient gap between two fast-moving vehicles is available in the approaching through traffic (ATT) stream. Even though, worldwide, temporal gaps are extensively used, the spatial gap is an important parameter that significantly affects minor street vehicles’ safety (U-turning vehicles in this study). The present research, undertaken in India, focuses on estimating the temporal and spatial critical gap of U-turning vehicles at uncontrolled MBMOs. The collected data were analyzed for six different types of U-turning vehicles at varying approaching through traffic volume (ATTV). For critical gap estimation, four different critical gap estimation techniques, namely, modified Raff method (MRM), Ashworth method, binary logit model (BLM) method, and occupancy time (OT) method were employed, separately. From the analysis, temporal and spatial critical gaps were observed to vary between different types of vehicle, and it was also observed to vary with ATTV. The critical gap values of this study were found to be smaller than the critical values reported in developed countries, signifying the aggressive driving nature of drivers in developing countries. A detailed appraisal of the different critical gap estimation methods has been carried out. From this analysis, the OT method was found to provide the closest critical gap values with the published literature compared with other methods. Lastly, the findings from the present study will be useful for capacity estimation and safety evaluation at MBMO.

Modern industrial manufacturing involves several manually and automated driven vehicles - not only for logistics and production purposes, but also for services, maintenance, resources supply and cleaning. These different types of vehicles are increasingly driven by electric powertrains that operate in the production halls, warehouses and other involved areas. Today, electric charging of these mobile devices is accomplished mainly manually and by use of a number of different not standardized charging interfaces, which leads to increased time and cost efforts. The paper evaluates different charging technologies for the use in industrial environments and introduces a new approach for automated, robot-controlled charging of electric vehicles, which is based on a standardized charging interface. The technology has been developed to fully automated charge different types of cars and other vehicles and consists of a vision system to identify the vehicle and the charging connector position in combination with a fully-controlled robotic system that plugs-in and -off the charging connector. In this way, the system is universally applicable for different types of autonomously and manually driven vehicles in a professional context, e.g. in production, logistics and warehouses.

The contributions of non-methane hydrocarbon emissions by different fuel type on-road vehicles based on tests in a heavily trafficked urban tunnel

Our way of life is on a collision course with geological limitations. Ever since petroleum geologist M. King Hubbard correctly predicted in l956 that U.S. oil production would reach a peak in l973 and then decline (1), scientists and engineers have known that worldwide oil production would follow a similar trend. Today, the only question is when the world peak will occur.The U.S. transportation system depends almost entirely (∼97%) on oil (2), and foreign imports have risen steadily since l973 as the demand increased and domestic supplies decreased. Today, more than 60% of U.S. oil consumption is imported and the dependence on foreign oil is bound to increase. There is no question that once the world peak is reached and oil production begins to drop, either alternative fuels will have to be supplied to make up the difference between demand and supply, or the cost of fuel will increase precipitously and create an unprecedented social and economic crisis for our entire transportation system.Among energy analysts the above scenario is not in dispute. There is, however, uncertainty about the timing. Bartlett (3) has developed a predictive model based on a Gaussian curve similar in shape to the data used by Hubbard as shown in Fig. 1. The predictive peak in world oil production depends only on the assumed total amount of recoverable reserves. According to a recent analysis by the Energy Information Agency (4), world ultimately recoverable oil reserves are between 2.2×1012 barrels (bbl) and 3.9×1012bbl with a mean estimate of the USGS at 3×1012bbl. But changing the total available reserve from 3×1012bbl to 4×1012bbl increases the predicted time of peak production by merely 11yr, from 2019 to 2030. The present trend of yearly increases in oil consumption, especially in China and India, shortens the window of opportunity for a managed transition to alternative fuels even further. Hence, irrespective of the actual amount of oil remaining in the ground, peak production will occur soon and the need for starting to supplement oil as the primary transportation fuel is urgent because an orderly transition to develop petroleum substitutes will take time and careful planning.Some analysts claim that hydrogen can take the place of petroleum in a future transportation system (56). But in previous publications, the authors have shown that hydrogen is inferior as an energy carrier to electricity (7) and that the energy efficiency of hydrogen vehicles, especially if the hydrogen were produced by the electrolysis of water, is considerably less than the efficiency of hybrid electric vehicles or fully electric battery vehicles (7). The results of these analyses have subsequently been confirmed by other studies, particularly those by Hammererschlag and Mazza (8) and Mazza and Hammerschlag (9).Before hydrogen could become a useful automotive fuel, an entirely new system of energy production and distribution on twice the scale of today’s electric power generating stations and distribution grid would have to be built. It has been estimated that a hydrogen transmission and storage system to fuel only 50% of the automotive fleet by the year 2020 would cost at least $600 billion (10) and that to make the hydrogen by electrolysis would require doubling the electric power generation rate (11). There is no question that a paradigm shift in fuel for worldwide transportation is imperative, and before embarking on such a huge investment, it is prudent to compare the hydrogen option with alternative ways to provide the energy and/or fuel needed by the transportation system.This paper presents and analyzes two generic approaches to meet the future demand of the U.S. ground transportation systems that do not require hydrogen, can use existing transmission infrastructure, and can eventually reduce CO2 emission drastically with a renewable energy system. Both these pathways are examined from an energetic and environmental perspective and are shown to be superior to the hydrogen economy on both these criteria. The first approach is a demand-side strategy based on the use of electric hybrid vehicles, an energy-efficient vehicle configuration, combined with a liquid fuel. This approach could use the existing liquid-fuel distribution system, but would need an expanded and robust electric-transmission system, albeit on a smaller and much more economical scale than a hydrogen fuel-cell infrastructure. The second approach is a supply-side strategy, based on synthetic fuel generation that can use initially coal or natural gas as the energy source, but can eventually transition to renewable biomass sources. The two pathways are not mutually exclusive, but can be combined into a secure and efficient future transportation system as will be shown in this paper.Cradle-to-grave energy efficiency is an important criterion for comparing energy-source utilization pathways because if a pathway is less efficient than another pathway that accomplishes the same final goal from the same amount of primary energy, then the less efficient pathway requires more primary energy to accomplish the same end. Hence, if the primary energy source is nonrenewable, then the less efficient pathway leaves less of the energy source for the future. It also means that more pollution is produced and the cost for the final end use is likely higher. However, if the primary energy source is renewable, then the efficiency does not change the amount of primary energy available in the future and energy efficiency does not have the same significance for renewable energy sources as for nonrenewable sources. Efficiency is, of course, important because the cost of delivering the energy is usually strongly influenced by the system efficiency. But a comparison between renewable and nonrenewable pathways should be based on economic and environmental criteria, such as cost and CO2 generation.In order to demonstrate the urgency for initiating a plan to supplement oil as soon as possible, we have made calculations to predict the potential gasoline savings based on the very optimistic scenario that, at an arbitrary starting time, all new light vehicles sold in the U.S. would be either hybrid or electric vehicles. The term “light vehicles” as used here includes all automobiles, family vans, sports utility vehicles, motorcycles, and pickup trucks. This scenario is an extreme case to show that because of the slow turnover of the light-vehicle fleet, it takes a long time for a significant impact on gasoline consumption to occur. The following cases are considered: (i) All new vehicles sold are gasoline-electric hybrid vehicles (HEV); (ii) all new vehicles sold are plug-in, gasoline-electric hybrids with a 20mil electric-only range (PHEV20); (iii) all new vehicles are diesel-electric hybrids (DHEV) with diesel fuel from coal or biomass; (iv) all new vehicles are plug-in, diesel hybrids with a 20mil all-electric range (PDHEV20); or (v) all new vehicles are all-electric vehicles (EV).The calculations use a rate of new vehicle sales of 7% of the fleet per year, a retirement rate of 5%/y, and a resulting net increase in total vehicles of 2%/y. These numbers represent an approximate fit to the light-vehicle sales and total number data for the years 1966 to 2003 reported by the U.S. government (12). All calculated results are presented in percentages and are therefore independent of the time at which all new vehicle sales switch to hybrids or EVs. When new car sales begin to be all hybrids or all EVs, it is assumed that the future rate of retirement of vehicles from the all-gasoline fleet is 5%/y of the remaining gasoline vehicles. The all-gasoline fleet is therefore completely retired 20 years later. The yearly rate of retirement of hybrid or EV vehicles is then 5% of the total number of vehicles at the beginning of that year, less 5% of the number of gasoline vehicles at the beginning of year zero. Thus, in year zero, no hybrid or EVs are retired.The following average vehicle mileage values were used: gasoline fleet, 21mpg (miles per gallon); gasoline HEV, 41mpg; gasoline PHEV 20, 56mpg of gasoline (13). A mileage is not needed for the EVs, or the diesels, since neither use gasoline, and we assume that the diesel fuel will be derived from nonpetroleum sources, as discussed in Secs. 34.The results of these calculations are presented in Figs. 234. Figure 2 shows the ratio of the total number of vehicles in the fleet, the number of all-gasoline vehicles in the fleet, and the number of hybrid or EV vehicles in the fleet to the total number in the fleet as a function of time. The total number of vehicles increases by over 60% in 25 years at the assumed 2%/y net increase while the number of all-gasoline vehicles decreases linearly from 100% initially to 0% after 20y. The number of hybrid or EV vehicles increases from 0% initially to 58% in 10y and 100% in 20y. This graph emphasizes how long it takes for the introduction of a new vehicle type to show a significant impact on the composition of the vehicle fleet, even when only the new vehicle types are sold after a starting point. This slow turnover of the fleet is the fundamental reason that the effects on gasoline consumption show up so slowly.Figure 3 shows the annual reduction in gasoline consumption as a function of time. Note that for HEVs the annual savings in gas consumption is 29% of the gasoline consumption for a conventional fleet in the tenth year and becomes constant at 49% in the twentieth year. Figure 3 also shows that the plug-in gasoline hybrid scenario saves 41% of the usage in the tenth year and increasing to 64% in the twentieth year and thereafter. Clearly, 10y after starting to sell only hybrid or EV vehicles, the impact of the HEV or PHEV20 scenarios on gasoline consumption is still rather small. After 20yr, the impact becomes significant, but gasoline consumption still remains high for gasoline hybrids. The total number of vehicles and the consumption (with the assumption of no efficiency improvement) by an all-gasoline fleet will have increased by more than 60%, but even the PHEV20 savings is only 40% of the zero-time annual-rate of gasoline consumption. The DHEV, DPHEV20, and EV scenarios show 59% annual savings in the tenth year and 100% in the twentieth year and thereafter. As would be expected, the nongasoline vehicles have a much greater impact on gasoline usage than gasoline-using HEVs, and the impact occurs more rapidly.Figure 4 gives the cumulative gasoline savings for the various scenarios compared to an all-gasoline fleet. HEVs save cumulatively 16% after 10yr and 20% after 20 years. Because of the cumulative savings, HEVs would use in 28yr the same amount of gasoline as an all-gasoline fleet would use in 20yr. PHEV20s save 21% after 10yr and 38% after 20yr. These results emphasize the relatively small effect on gasoline consumption that these highly optimistic scenarios have in the first decade after implementation. DHEVs, DPHEV20s, and EVs, the options without any gasoline use, save cumulatively as much as 32% after 10yr and 59% after 20yr.A 2004 report of the Committee on Alternatives and Strategies for Future Hydrogen Production and Use (14), prepared under the auspices of the National Research Council (NRC), concluded that the vision of a hydrogen economy is based on the expectation that hydrogen can be produced from domestic energy sources in a manner that is “both affordable and environmentally benign.” An analysis of currently available technologies for achieving this goal (7) showed that irrespective of whether fossil fuels, nuclear fuels or renewable technologies are used as the primary energy source, hydrogen is inefficient compared to using the electric power or heat from any of these sources directly. Given these facts, it is important to note that the NRC report also stated that “If battery technology improves dramatically, all-electric vehicles might become the preferred alternative (to fuel cell electric vehicles).” The report also noted that “Hybrid vehicle technology is commercially available today and can therefore be realized immediately.” If synthetic fuels made from coal, natural gas, or biomass were used in place of gasoline in hybrid vehicles, the consumption of oil could be reduced immediately and eventually eliminated. In the light of these observations, it is therefore important to examine what the current state of battery technology is, what can be expected in the near future, and how these developments affect the potential of hybrid vehicle performance and economics.To assess the performance of a battery for electric vehicles, the following characteristics have to be considered: Specific energy, a measure of the battery weight in units of watt hours per kilogramEnergy density, a measure of the space the battery occupies in watt hours per cubic meterCapacity, the total quantity of energy a battery can store and later deliver in watt hoursEfficiency, the ratio of energy that can be extracted from the battery to the initial energy input to change the batterySpecific power, the rate at which the battery can deliver the stored energy per unit weight of battery in watts per kilogramBattery lifecycle, the number of charge and discharge cycles that a battery can sustain during its lifeA significant effort to replace oil as a transportation fuel was undertaken ten years ago in California, when the California Air Resources Board [CARB] mandated that a certain percentage of all vehicles sold in California had to have zero tailpipe emissions (15). At that time the only technology available to meet the mandate was the all battery electric vehicle [BEV], which required no gasoline for its operation. The experiment to mandate the use of BEVs in California failed because the technology was not ready for commercialization. The best battery available in 1995 (fluted-tubular lead acid) had an energy storage density of 35Wh∕kg, a specific power of 100W∕kg, and a life cycle of 600-1000cycles. With these battery characteristics, the maximum range of a BEV was only 50mil, and the battery pack required replacement every 25,000mil at a cost of between $7000 and $8000 for an average BEV (16). Since that time, new batteries have been developed by Panasonic, VARTA, and SAFT, that have twice the energy-storage density, three times the specific power, and two or three times the cycle life of the lead acid batteries sold in California, as shown in Table 1 (13).In addition to the advanced batteries, a new concept has been developed that combines the best qualities of hybrid and battery vehicle technologies. This “plug-in hybrid vehicle” can recharge vehicle batteries during off-peak hours, and since most cars are parked 90% of the time, there are plenty of charging opportunities at both home and the workplace. Furthermore, a large portion of the electric generation infrastructure is only needed for peak demands and lays idle much of the time. Hence, if charging automobile batteries occurred during off-peak hours, they would level out the load of the electric production system and reduce the average cost of electricity (17). Moreover, plug-in hybrid vehicles are not range limited because they have an engine that can refuel at existing gas stations to use when the batteries are low.The efficiency of a PHEV depends on the number of miles the vehicle travels on liquid fuel and electricity, respectively, as well as on the efficiency of the prime movers according to1η=energytowheelsenergyfromprimarysource=f1η1η2+f2η3η4where η1 is the efficiency of the primary source of electricity, η2 is the efficiency of transmitting electricity to the wheels, f1 is the fraction of energy supplied by electricity, f2 is the fraction of energy supplied by fuel =(1−f1), η3 is the efficiency of primary source to fuel, and η4 is the efficiency of fuel to wheels.PHEVs can be designed with different all-electric ranges. The distance, in miles, that a PHEV can travel on batteries alone is denoted by a number after PHEV. Thus, a PHEV20 can travel 20mil on fully charged batteries without using the gasoline engine. According to a study by EPRI (13), on average 1/3 of the annual mileage of a PHEV20 is supplied by electricity and 2/3 by gasoline. The percentage depends, of course, on the vehicle design and the capacity of the batteries on the vehicle. A PHEV60 can travel 60mil on batteries alone, and the percentage of electric miles will be greater as will the battery capacity.The tank-to-wheel (more appropriately, battery-to-wheel) efficiency for a battery all-electric vehicle according to EPRI (13) is 0.82. In a previous analysis by the authors (18), the efficiency in 1993 was only 0.49. Comparing these results shows the enormous improvements in the electric component efficiency (controller 87%, battery 90%, charger 90%, drivetrain 90%;). When these numbers are multiplied by a hybrid-weight-times-idle factor of 1.3 (19), the overall efficiency of an electric hybrid is 82%, the same as that used in the EPRI study (13). It is important to note that currently all-electric vehicles can be nearly twice as efficient as when (18) was published.Given the potentials for plug-in hybrid vehicles, the Electric Power Research Institute (13) conducted a large-scale analysis of the cost, the battery requirements, and the economic competitiveness of plug in vehicles today and within the near term future. Table 2 presents the net present value of life-cycle costs over ten years for a midsized combustion vehicle [CV], hybrid vehicle [HEV] and a plug in electric vehicle with a 20mil electric-only range [PHEV20]. The battery module cost in dollars per kilowatt is the cost at which the total life-cycle costs of all three vehicles would be the Figure presents cost for battery as a function of number of units produced per year. According to this a production of about units per year units would the cost reduction to make both hybrid electric vehicles and plug in electric vehicles 3 presents the electric and plug-in hybrid vehicle battery that would be to make electric vehicles cost for vehicles according to EPRI (13). As shown in Table the characteristics of batteries, and batteries are to meet the required cost and performance The battery characteristics shown in Table 1 and Fig. are years and it is likely that more from would show Furthermore, the EPRI study assumed a current gasoline cost of A of the analysis based on a gasoline cost of that the battery at which the net present values of conventional combustion vehicles and battery vehicles are would up from to for an HEV and from to for a PHEV Figure shows the cost for batteries production for Hence, it that the cost of HEVs and with available batteries is with that of engine The EPRI analysis is because it compared the performance of all battery electric and plug in hybrid vehicles only to currently available combustion as shown in the use of diesel in a hybrid would increase the efficiency of compared to a hybrid with engine and the amount of fuel Hence, it be concluded that the EPRI analysis is it includes advanced batteries, it does not the increased efficiency by using diesel of combustion Furthermore, diesel fuel, as will be shown in can be produced from coal or renewable sources as can the electric power required for charging the The introduction of to the energy is the of this it is and can be as renewable technologies become more cost and fossil fuels more natural gas and biomass can be into liquid the most fossil fuel in the is used almost to In order to make coal into a vehicle fuel, it first be to a gas by a of The of this then be to of that can be used as vehicle fuel. biomass and natural gas can be used of coal or combined with coal to make these and are discussed gas can be used as a vehicle fuel, or it can be with to make gas, which can be used to fuels in the same manner as for The technology is well developed as shown by the recent of of which will natural gas, which is currently to liquid fuel. These and a in of which is diesel in With a with an estimated billion and a diesel with the of with an estimated at The of natural gas to make vehicle fuels was discussed in an paper by the authors (18), and of those results are presented later for comparison with coal as the fuel It should also be noted that biomass can be either alone or in with coal and to liquid fuels by the same as coal, or it can also be and then into vehicle fuels as in is a that is a in the production of synthetic liquid fuels from coal for transportation The coal is shown in Fig. It a such as coal or with to and This gas can be to hydrogen or to make or can be used as a transportation fuel in but this study on diesel fuel because are more the first of the coal is with limited to and The in the coal is to hydrogen gas, and are as In the shift is with to and The and hydrogen are from the and to the or into The that is in this is from the in a for Thus, it can be from the and are the costs when liquid fuel is produced from The estimated time of for a is to years. The depends on the production capacity of the the cost of a with a capacity to barrels of liquid fuel per is estimated to be of the order billion of coal claim that there will be gas pollution from the However, in the future vehicle emissions of can be reduced those of vehicles, by the use of plug-in hybrid electric vehicles and by of the from the fuel production is a synthetic diesel fuel that can be made from coal by of The is first to make which can then be to The is to the and the gas is to electricity for the as shown in Fig. is a gas at but can be under and then can be to other liquid of make it an fuel for It is similar to but has a number The number to the of a fuel to With combustion of the fuel occurs after and emissions are as a of combustion The combustion also in by the need for to the shown in Fig. coal into liquid fuel. The was by scientists before and is used today in by to make diesel fuel gas to make a liquid fuel of synthetic diesel fuel, which is similar to and which is used to make synthetic gasoline (7). The is from the liquid diesel and to the The gas resulting from is to electricity for the can be made from coal by by gas After the hydrogen gas and are from the gas, and hydrogen are The hydrogen can be stored and the can be for electricity and/or to the shift as shown in Fig. store and the hydrogen, it is either to it to or to it at a The efficiency of the first option is while the second is efficient (7). Both and hydrogen have been for fuel storage in a of hydrogen fuel-cell vehicles is in the of coal or natural gas into a vehicle fuel. The energy efficiency of these is important in the overall well to efficiency of these alternative Table 4 presents or efficiency for various fuels from coal or natural and have reported the and energy for with of the and values are used (18) presented for natural gas without and estimated that of CO2 the efficiency of by about two percentage Since natural gas only about as much per unit of energy as coal, it has been assumed that will reduce the efficiency of to fuels by percentage point. Thus, percentage has been from values reported by (18) to the values shown in Table In the of data for the of natural gas to the authors assumed that the ratio of the for natural gas is the same as that for coal to estimate this efficiency as shown in Table 4 that the production of liquid fuels from natural gas is more efficient than from But is in and the technology is not a It is however, for the that is currently into the in gasoline The of of these has been But production is the more for the term and does not require hydrogen as a fuel or energy Today, the of fuel from coal, at the only in The of supplies of such fuels as gasoline, and The economic and of coal have been U.S. and for a fuel in using technology and are to the that will have a capacity to of diesel fuel. has in recent NRC study other technologies that could synthetic fuels from biomass and presents a comparison of the energy on energy for production from and These significant in synthetic But the for synthetic fuel production need to be multiplied before synthetic fuels can make up for the between demand and of gasoline after the peak in oil production is on the analysis presented in this we the following and oil production is expected to peak within the and as is liquid fuel are expected to increase This could lead to a crisis in the U.S. transportation system that on 60% of which is options for a transportation crisis by and/or liquid fuels derived from petroleum with synthetic fuels from natural gas, or coal and by demand by increasing the efficiency and mileage of options to have impact they be at least before hybrid vehicles are a option to reduce the liquid fuel consumption of future transportation hybrid vehicles can the existing infrastructure for electric power transmission by charging batteries during peak hours and use liquid fuels only for a fraction of overall power hybrid vehicles can diesel that can be by synthetic fuels derived from coal, natural gas, or use efficiency is increased efficiency alone will not be to the transportation without the production of large of synthetic liquid number of technologies for synthetic diesel that can be used in diesel and reduce emission of that lead to scale of effort required to provide synthetic fuels will require years to and should therefore be as soon as hybrid or all-electric vehicles with available battery technology in an are compared to gasoline of the of the transportation it is that be by government such as for the of synthetic fuels and CO2 high liquid fuel mileage for automobiles, and for efficient plug-in hybrid scenario in this paper for a secure transportation system can be immediately with available technologies and without hydrogen or authors to for as of an independent study for the of at the of

As autonomous driving technology advances, future intersections will witness the coexistence of two distinct vehicle categories: connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). Since different types of vehicles bring varying benefits to the transportation system, assigning the same right-of-way to different types of vehicles would result in unfairness in the transportation system. To align the traffic priority of different types of vehicles with their benefits to the transportation system, this study proposes a signal optimization model that takes into account the relative fairness factors and is solved using an improved particle swarm algorithm. On the one hand, this study introduces an archive module to improve the uniformity and stability of the improved algorithm results. On the other hand, this study incorporates a decision-maker preference module to improve the accuracy of the improved algorithm for the preference region. Finally, the Simulation of Urban Mobility (SUMO) software was utilized to conduct experiments under two conditions: the same permeability of each arm traffic flow and the different permeability of each arm traffic flow. The results indicate that the proposed optimization method is capable of refining the signal control strategy with the traffic benefits conferred by different types of vehicles. Under the different arm permeability set in this study, the proposed signal optimization method can reduce the average delay time for CAVs by 23.86%, thereby facilitating the deployment of CAVs.

Road transportation is identified as one of the primary contributors to urban carbon emissions. This study employed a localized MOVES model, integrating high-resolution urban vehicle data, along with a categorical LMDI decomposition model, to conduct a detailed bottom-up analysis of carbon emissions from passenger and freight vehicles in Xi’an. The MOVES model was utilized to evaluate the inter annual variability of carbon emissions from road transportation sources. The LMDI models assessed the impact of six factors, focusing on microscopic traffic activities such as passenger/freight volume and turnover, vehicle ownership, mileage, carbon emission rate, vehicle types, and fuel types. The research indicates that carbon emissions in Xi’an increased significantly between 2012 and 2021, rising from 21.95 to 51.15 million tons, with an average annual growth rate of 9.9%. Passenger vehicles are the primary contributors to this increase. The emission patterns of passenger and freight vehicles differ notably due to various factors. For passenger transport, which includes passenger cars and school buses accounting for 97% of emissions, per capita vehicle turnover is the primary influencing factor. In contrast, for freight transport, combination long-haul trucks dominate emissions, comprising 65% of total freight emissions, with freight volume being the key influencing factor. By analyzing the factors driving carbon emissions from passenger and freight vehicles, this study provides valuable insights for reducing road traffic emissions in Xi’an and similar regions.

The traffic flow structure consists of different types of vehicles. The data taken in the process of the pavement design, which indicates the type of vehicle, is called AADT. AADT is the average annual daily traffic. The same AADT value can result in a different thickness of pavement, which is reflected in a different percentage of certain types of vehicles. For the traffic load design according to standard SRPS U.C4.010, it is important to know the rate of change in the number of heavy truck vehicles. The average annual rate of change in the number of heavy truck vehicles is used in design of the load. An analysis of the change in the number of heavy truck vehicles has been made for state roads in Serbia where traffic is counted. The paper presents an overview of the method of counting traffic on state roads. The average percent change in the number of heavy truck vehicles in the period 2014-2017 was analyzed.

The Comparison Of Emission Levels Of Carbon Monoxide Exhaust Gas In Automatic Motor Vehicle Injection System. Transportation plays a very big role as a source of air pollution, especially exhaust emissions reach 60-70%. CO emitted by motor vehicle which exceed the standard be able to cause disturbance and even death. The development of a two-stroke engine vehicle into a four-step engine is a people demand who want a vehicle which is easy and comfortable. Indonesia has many different types of vehicles, but two types of motor vehicles with the YMJET-FI and PGM-FI system are most on the market. Three fuels such as premium, pertalite and pertamax are used by the public. This research was observational analytic, using Posttest Only Design to analyze the CO emission from YMJET-FI and PGM-FI engine with premium fuel, pertalite and pertamax. CO emission was tested at engine speed of 1.900-2.000 rpm and temperature 80oC. Analysis used Two Way Analysis of Variance statistic test with α = 0,05%. The results showed that 
 YMJET FI using premium emitted CO as 0,23%, pertalite 0,32% and pertamax 0,37%. PGM-FI using premium emitted 0,21% , pertalite 0,24% and pertamax 0,25%. The conclussion is, both of YMJET-FI and PGM-FI, using premium, pertalite and pertamax emitted CO lower than the emission quality standard according to KemenLH 2009 such as of 4,5%.

Transport sector is among the top air pollution contributors in the Indian cities. In this study, we present our work in developing a high-resolution on-road transportation emission inventory for the Indore city in India and developing a mitigation plan as part of the Clean Air Catalyst (CAC) program. The CAC program, led by WRI and EDF, aims to build capacity for local air pollution solutions through source apportionment and emission inventory. Transport sector emissions in the present study are estimated by using a combination of secondary and primary data. The secondary data available from various sources for the Indore city to generate a high-resolution emission inventory from the transport sector was the year wise vehicle registered, ward wise population, vehicle number at ward level. Data like purpose of travel, second- hand vehicle seller’s information, combined use of secondary and primary data to estimate the vehicle age are taken to reduce the uncertainty existing in the earlier methods, which helped in generating locally tailored emission inventory and solutions. To estimate Socio economic status (SES) and land use centric neighborhood emissions, a traffic count survey was also conducted to determine the number of different types of vehicles on the road in the Indore city. For conducting traffic vehicle count and Origin-Destination (OD) survey, six outer cordon points (OC) situated on highways outside the municipal limits were selected to understand the vehicle movement from the city to the outside and vice versa. Selected 11 inner cordon points (IC) inside the city covered all types of land use patterns, which represented roads from the residential settlement, commercial areas, industrial hubs, and mixed land use. These roads consisted of all types of road hierarchy comprising arterial (major roads), sub-arterial (connecting roads), local (minor roads), and highways which interconnect Indore into surrounding districts. The findings revealed that the transport sector along with road dust is the highest contributor in the Indore city, which is around 70% of the total PM2.5 emissions. The exhaust emissions in high SES wards ranges from 0.36- 40 MT/year, medium SES is from 0.13- 18 MT/yr, industrial regions is 0.015- 34 MT/year, and low SES from 0.6 – 54 MT/yr. Non exhaust emissions for high SES range from 1.3- 146 MT/year, medium SES 0.6- 63 MT/year, industrial regions 0.05- 121 MT/year and low SES range from 2.2- 194 MT/ year. These emissions were further used to design solutions for the transport sector at ward level. Pilot wards with high emissions were selected to implement locally tailored solutions designed for the city such as the low emission zones (LEZ), enhancing public transport service (PTS), scrapping of vintage vehicles and developing Infrastructure for NMT (sidewalks, bicycle lanes, and secure bicycle parking facilities, traffic calming to make NMT safe). The integrated approach of high-resolution emission inventory and locally tailored solutions can also be utilized in other areas/cities to address air pollution.