N UMBER OF CARS AND VEHICLE TYPE CHOICES

Several recent empirical findings provide evidence on the relationship between a partic- ular neighborhood feature, population density, and the vehicle fuel efficiency chosen by the resident, controlling for the resident’s other characteristics such as income. People residing in less dense suburban areas drive more than people in high density areas, and their fuel consumption shows a larger proportional increase than vehicle miles traveled (Brownstone and Golob (2009), Kim and Brownstone (2012)). This disproportionate increase in fuel usage suggests that households residing in less dense areas are more likely to use less fuel-efficient (bigger) vehicles than households in denser areas. Unlike Brownstone and Golob (2009), where vehicle choice is just implicit, Fang (2008) explicitly models vehicle choices, with res- idential density used as a key explanatory variable. Fang (2008) finds that when density increases, people tend to switch from trucks to cars and from large-size cars to small-size cars, which implies a positive correlation between residential density and vehicle fuel effi- ciency. Moreover, West (2004) finds that people choosing a less fuel-efficient bigger car tend to utilize the vehicle more and are usually located in the mid-west and south of the US, where residential density is lower. Reinforcing these findings, using a global sample of 32 cities, Newman and Kenworthy (1989) find a disproportionate increase in fuel consumption in cities with low density, where automobile dependency is high.

In our model, the cost of driving inconvenience captures these incremental costs of travel in high density areas. The goal is to analyze the consumer’s joint decision on land consump- tion and vehicle size (fuel efficiency) from an urban economics perspective, reflecting the empirical literature described above. As far as we know, no urban models exist that focus on this joint decision. 2 In the model, the consumer explicitly considers driving inconvenience, a kind of subjective commuting cost indicating how much the driver dislikes driving, in the choices of land consumption, residential location, and vehicle size. Driving inconvenience is assumed to be influenced by vehicle size (inverse of fuel efficiency) and residential den- sity. A larger vehicle gives lower driving inconvenience, which reflects the greater comfort and safety offered by larger cars, holding neighborhood density fixed. We assume that high neighborhood density gives higher driving inconvenience, motivated by higher parking cost, congestion, and a worse driving environment in denser areas. Commuting cost per mile is then comprised of driving inconvenience and monetary costs related to vehicle size, including fuel costs. A resident chooses an optimal vehicle size that yields the lowest commuting cost per mile. The resulting minimized commuting cost per mile is expressed as a function of residential density.

This section explains the process of data communications via the OCC link. LED light sources can offer both communication and illumination performance. Because the human eye cannot perceive the light pulses blinking at 200 Hz or higher frequency, a Manchester encoding method with a flickering rate of greater than or equal to 200 Hz was used. As shown in Figure 4, we set a data frame consisting of 8 bits of preamble and 16 bits of information data. In this study, we set the bit sequence of preamble as ‘10101010’. For information data, we consider two categories, ‘action data’ and ‘vehicle type data’. To provide enhanced productivity and convenience by freeing occupants from the responsibility of driving, vehicles should anticipate the action of other vehicles and collect information of the vehicle type. Using these data communications via wireless optical links, OCC systems ensures the safety of driving. As a transmitting LED source, daytime running lights (DRLs), which are used to increase the visibility of vehicles so that other drivers can see on the road, are used.

During the end of 1998 and the beginning of 1999, 3350 questionnaires were distributed among drivers and fleet-operators of cars, buses, and trucks. Drivers were randomly approached at different gas stations along Dutch motorways during different days, as the systems questioned in the questionnaire were presented to be for motorway-use only. Truck and bus drivers were approached by mail, as most of these drivers take fuel at their company location. Fleet- operators were selected from the register of the Dutch Chamber of Commerce and from databases of branch organisations, and were approached by mail. A total of 485 questionnaires was returned within a 6-weeks' period, implying an average degree of response of 14.5%. This rate seems reasonable, given the high complexity of the questionnaire in combination with the fact that even 80% of

The first set of changes relates to the cost of parking a car at work. In the data underlying the statistical model of Chapter 3, the cost to park a car at work is $15 per day for the business district of Manhattan and $0 for all other areas of New York City. While this is clearly a simplification of reality, it does capture the essence of the daytime parking price scheme in the city. Because the cost to park a vehicle in this situation swamps all other costs of commuting, many sustainable cities advocates have suggested raising parking prices at employment centers that are accessible by transport modes other than the car. This scenario is crudely represented by raising parking prices to Manhattan business district levels throughout the city. A second scenario modeled here is reducing all parking prices in New York City to $0. This is clearly not a scenario that will actually happen - parking prices will remain high in Manhattan. However, since most other employment centers in the United States actually do provide free parking, it is interesting to see how this simulation model predicts New Yorkers would react to such a situation.

Car buyers in California now have a lot of choices when selecting a car to buy that causes less air pollution. Refer to the EPA Green Vehicle Guide at: www.epa.gov/autoemissions/select.htm and www.fypower.org/save_gasoline/ websites to look at different car models and the amount of smog and green house gases each produces.

opportunities for lower emission choices. 25 Notwithstanding that turnover, the 2012 AP2 report had forecast transport emissions to grow to 2020, as discussed further below. 26 The first performance report of the GVDS was released for the period “late 2008 until mid-2011” in the 2012 AP2 report indicating only movement in vehicle sales between green vehicle ratings for the period. 27 The reduction in average emissions intensity for new vehicles acquired in this period was not provided, making it difficult to assess the environmental effectiveness of the GVDS. The 2015 research paper stated that, although the GVDS was introduced on 3 September 2008, it was not until 2012 that the ACT Government released the performance report. No review or changes were made to the tax design or price signal of the GVDS and no further performance report was released since June 2011.

From a modeling and forecasting perspective we find that even though only 33% of the households had hybrids at the time of purchasing the PEV hybrids can be used to predict both market growth and spatial distribution of PEVs. Overall we can see two different types of PEV owners, the people who didn’t purchase a vehicle for a long period, even though they had the income to do so and the second type, households who purchase new vehicles regularly and purchasing the PEV was a change in vehicle type purchased but not a change in purchasing habits. The motivation for buying the car may be similar in both cases but the potential market size and the future purchases of these households may differ.

Passenger Vehicle Occupant Fatality Rates by Type and Size of Vehicle By Rajesh Subramanian * Summary The National Highway Traffic Safety Administration (NHTSA) has routinely published (NHTSA, 2005) passenger vehicle occupant fatality rates, both overall and in vehicles that rolled over, by the type of the vehicle, such as passenger cars, SUVs, pickup trucks or vans. These categories of vehicles are broad and may mask differences in rates that might exist between vehicles of different sizes within a type.

It contends that this encouragement should be in the form of a strong price signal conveyed to new motor vehicle buyers at the time of acquisition to facilitate a behavioural shift toward buying fuel-efficient, lower-CO 2 -emitting vehicles. Given that buying a car is one of the largest purchase considerations most people will make, 18 that the average life span of the vehicle chosen can be 20 years, and that about 4% of the fleet is retired each year, 19 a strong price signal will be an important mechanism for behavioural change. In the 1.7 million households that purchased a passenger vehicle between March 2011 and March 2012, purchase cost was considered to be a key factor (58%), followed by fuel economy and running costs. 20 In terms of non- financial factors, size and type of vehicle were the next most important considerations. 21 However, despite increased public awareness of the effect of greenhouse gases from passenger vehicles, environmental impact and carbon emissions were the least important considerations when buying a car in Australia in 2012. 22

Faster vehicle speeds or velocities are not only associated with an increased risk for crash occurrence ( Moore et al., 1995; Zivot and Di Maio, 1993 ), but faster speeds are also related to more fatalities and more severe injuries among ve- hicle occupants ( Ashton et al., 1978; Tolonen et al., 1984; Buzeman et al., 1998 ). Pedestrians, who do not have the ben- efit of vehicle safety devices (seat belts or air bags) or even the protection of the automobile’s frame, are more vulnera- ble to injuries and death when struck. In car occupant stud- ies, when vehicles with larger masses collide with smaller cars, the passengers in the smaller vehicles are more seri- ously injured ( Grime and Hutchinson, 1979 ). Lower vehicle weight is associated with a higher net risk to occupants in collisions with substantially larger and stronger objects and also with a lower net risk for much smaller and more vulner- able entities such as pedestrians, bicycles, and motorcycles when struck ( Insurance Institute for Highway Safety, 1998 ). In published pedestrian injury studies, it generally has been observed that larger cars result in more serious pedestrian injuries ( Galloway and Patel, 1982; Atkins et al., 1988 ) and higher pedestrian fatality rates ( Mizuno and Kajzer, 1999 ).

Working Well—Specifically, WELCOA focuses on building Well Workplaces—organizations that are dedicated to the health of their employees. The Well Workplace process provides business leaders and members with a structure or blue print to help their organizations build results-oriented wellness programs. Ultimately these programs help employees make better lifestyle choices, and positively impact the organization’s bottom line. To date, over 700 companies have received the prestigious Well Workplace award. In addition, nine cities have been designated as Well Cities—Jacksonville, FL; Omaha, NE; Chattanooga, TN; Hobart, IN; Lincoln, NE; Kearney, NE; Kanawha Valley, WV; and Gainesville, FL and Bangor, ME—while several other cities have made the commitment to join this exclusive group.

Working Well—Specifically, WELCOA focuses on building Well Workplaces—organizations that are dedicated to the health of their employees. The Well Workplace process provides business leaders and members with a structure or blue print to help their organizations build results-oriented wellness programs. Ultimately these programs help employees make better lifestyle choices, and positively impact the organization’s bottom line. To date, over 700 companies have received the prestigious Well Workplace award. In addition, nine cities have been designated as Well Cities—Jacksonville, FL; Omaha, NE; Chattanooga, TN; Hobart, IN; Lincoln, NE; Kearney, NE; Kanawha Valley, WV; and Gainesville, FL and Bangor, ME—while several other cities have made the commitment to join this exclusive group.

© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1183 7.2.3 SSTS to CTS Roll performance is used as an example here to illustrate the SSTS to CTS flow down process. In the second process, the vehicle technical specification of body roll gradient of 5 deg/g is the design target. 780 Nm/deg front roll stiffness and 410 Nm/deg roll stiffness are set as part of front and rear suspension subsystem specifications. The third phase of the process is to set the design parameters for the front and rear suspensions individual components in order to achieve the roll stiffness while meeting all the other subsystem specifications such as ride rate. In order to meet the roll stiffness requirements, suspension key parameters such as spring rate, roll-center height, and stabilizer bar size are defined. In the process of defining these key parameters, suspension ride frequency and packaging requirements need to be balanced carefully to obtain an optimum chassis performance. In order to determine the suspension components specification, suspension simulation model ADAMS [7] is used to determine the roll stiffness of the suspension from the suspension components. Figure 4 shows the ADAMS model of the front and rear suspension of a compact passenger vehicle.

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Cargo Area Length @ Floor to Seat 1 N/Ain Cargo Area Length @ Floor to Seat 2 N/Ain Cargo Area Length @ Floor to Seat 3 N/Ain Cargo Area Width @ Beltline N/Ain Cargo Box (Area) Height N/Ain Cargo Box Width @ Wheelhousings N/Ain Cargo Volume to Seat 1 54.6ft³ Cargo Volume to Seat 2 17.7ft³ Cargo Volume to Seat 3 17.7ft³

Cargo Area Length @ Floor to Seat 1 N/Ain Cargo Area Length @ Floor to Seat 2 N/Ain Cargo Area Length @ Floor to Seat 3 N/Ain Cargo Area Width @ Beltline N/Ain Cargo Box (Area) Height N/Ain Cargo Box Width @ Floor N/Ain Cargo Box Width @ Top, Rear N/Ain Cargo Box Width @ Wheelhousings N/Ain Cargo Volume N/Aft³

Manual Anti-Whiplash Adjustable Front Head Restraints and Fixed Rear Head Restraints Memory Settings -inc: Driver Seat, Door Mirrors and Steering Wheel. Open Pore Radica Wood Trim Outsid[r]

Cargo Area Length @ Floor to Seat 1 86.2in Cargo Area Length @ Floor to Seat 2 49.1in Cargo Area Length @ Floor to Seat 3 20.2in Cargo Area Width @ Beltline 50.2in Cargo Box (Area) Height 36.7in Cargo Box Width @ Floor N/Ain Cargo Box Width @ Top, Rear N/Ain Cargo Box Width @ Wheelhousings N/Ain Cargo Volume N/Aft³