Article

Modeling the effect of electric vehicle adoption on pedestrian traffic safety: An agent-based approach

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Abstract

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles.

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Modern computerized vehicles offer the possibility of changing vehicle parameters with the aim of creating a novel driving experience, such as an increased feeling of sportiness. For example, electric vehicles can be designed to provide an artificial sound, and the throttle mapping can be adjusted to give drivers the illusion that they are driving a sports vehicle (i.e., without altering the vehicle’s performance envelope). However, a fundamental safety-related question is how drivers perceive and respond to vehicle parameter adjustments. As of today, human-subject research on throttle mapping is unavailable, whereas research on sound enhancement is mostly conducted in listening rooms, which provides no insight into how drivers respond to the auditory cues. This study investigated how perceived sportiness and driving behavior are affected by adjustments in vehicle sound and throttle mapping. Through a within-subject simulator-based experiment, we investigated (1) Modified Throttle Mapping (MTM), (2) Artificial Engine Sound (AES) via a virtually elevated rpm, and (3) MTM and AES combined, relative to (4) a Baseline condition and (5) a Sports car that offered increased engine power. Results showed that, compared to Baseline, AES and MTM-AES increased perceived sportiness and yielded a lower speed variability in curves. Furthermore, MTM and MTM-AES caused higher vehicle acceleration than Baseline during the first second of driving away from a standstill. Mean speed and comfort ratings were unaffected by MTM and AES. The highest sportiness ratings and fastest driving speeds were obtained for the Sports car. In conclusion, the sound enhancement not only increased the perception of sportiness but also improved drivers’ speed control performance, suggesting that sound is used by drivers as functional feedback. The fact that MTM did not affect the mean driving speed indicates that drivers adapted their “gain” to the new throttle mapping and were not susceptible to risk compensation. 1. Introduction Drivers use their vehicles as more than just a means to arrive at their destinations. As explained by Rothengatter [1]; road user behavior is to an extent governed by the “pleasure of driving fast” (p. 605). Indeed, a portion of road users appears to be attracted to sporty driving, as evidenced by the sales of sports cars or vehicle models that offer high engine power and agile handling characteristics [2]. As an alternative, several manufacturers produce vehicles that can provide a sporty driving experience via a sport mode the driver can select. The sport mode has gained a substantial presence on the car market today [3–8]. According to manufacturers, the sport mode “permits an increased responsiveness from the engine and the gearbox” [7] and offers a “sporty driving style” [5]. The sport mode may encompass technology that increases the throttle sensitivity, road holding, and agility of the vehicle [9–11]. This includes the active drivetrain, for example, changes in engine mapping and gear shifting [12, 13], active suspension, and four-wheel steering [14, 15]. Additionally, sport modes can be accompanied by mechanical sound enhancement, which concerns the adjustment of physical elements of the drivetrain and the active control of valves that redirect the engine airflow and influence the exhaust sound [16, 17]. In recent decades, several techniques have been developed to increase perceived sportiness without altering the vehicle dynamics and without requiring costly components or mechanical adjustments to the vehicle. Two of such techniques are Artificial Engine Sound and Modified Throttle Mapping. 1.1. Artificial Engine Sound (AES) Artificial Engine Sound (AES) refers to a system that produces synthetic sounds through the cabin speakers. AES has been proposed for electric vehicles (e.g., [18–22]). However, current research on sounds for electric vehicles mostly focuses on pedestrian safety (e.g., [23, 24]). Considerably less research is available that focuses on the experience of drivers inside the electric vehicle. Psychoacoustics research has shown that perceived sportiness can be increased by adjusting characteristics of the sounds, such as loudness, roughness, sharpness, and tonality [25, 26]. However, a limitation of psychoacoustics studies is that they are typically conducted in listening rooms. As Jennings et al. ([27], (p. 1263)) argued, “perception of the sounds of on-road cars is affected by stimuli for other senses (e.g., visual and vibrational), and the fact that an assessor is also concentrating on driving.” To illustrate, research in a listening room by Park et al. [28] found that loudness was predictive of perceived sportiness (r = 0.84) but negatively predictive of perceived comfort (r = −0.83), consistent with the generally accepted “trade-off hypothesis of pleasantness and power” ([29] p. 1203). A driving simulator study by Hellier et al. [30], however, found that drivers regarded no engine noise at all as uncomfortable. Hence, it appears that sound perception may be different in listening rooms as compared to active driving. Very little research on perceived sportiness in real vehicles is available. An exception is Zeitler and Zeller [31], who let acoustical experts rate the interior sounds of different vehicles on a test track. Their results showed that perceived sportiness was strongly correlated with the sound volume increase during engine load (i.e., while accelerating). However, engine performance (e.g., actual sportiness) and acoustic feedback were confounded; that is, the vehicles that delivered more power were also those that produced a sporty sound. In a follow-up experiment, they tried to disentangle these two effects using AES and found that vehicle sounds and engine torque independently contributed to perceived sportiness. Apart from investigating the effects of AES on perceived sportiness, it is essential to examine the extent to which AES influences driving behavior. Previous research suggests that the presence and volume of vehicle sound affect driving speeds. More specifically, it has been found that a reduction in engine volume or the lack of engine sound causes drivers to drive faster [30, 32], underestimate their speed [32–34], and show poorer speed control [35–37]. These findings are consistent with the notion that engine sound acts as an information source that facilitates perception and control, or as argued by Hellier et al. ([30] p. 598), “engine noise can be characterised as “feedback” rather than “noise.”” In summary, although the above-mentioned studies indicate that the presence and volume of sound affect driving behavior, there appears to be a lack of research about how drivers perceive and respond to sound enhancement techniques that could be applied in electric vehicles, such as AES. Furthermore, research on vehicle sound has to date been predominantly conducted in listening rooms, a setting that cannot provide information about drivers’ speed adaptation. 1.2. Modified Throttle Mapping (MTM) A second approach that may increase perceived sportiness without requiring mechanical components is Modified Throttle Mapping (MTM). MTM is defined as the software-based adjustment of the relationship between the driver’s throttle input and the engine throttle input. Through MTM, for a given driver throttle input, the engine produces more torque while the maximum torque (i.e., the torque for 100% driver throttle input) remains the same. Note that MTM is not the same as modified “engine mapping,” that is, the adjustment of engine characteristics through changes in fuel injection, air charge, ignition timing, and valve timing and other factors that influence engine performance [38, 39]. Research describes different ways of changing the throttle mapping and the corresponding effect on vehicle performance (e.g., [10, 40, 41]), but only a few studies have investigated the effects of MTM on driving behavior. The few studies that did investigate human-in-the-loop effects of MTM used intelligent controllers, such as a throttle pedal for regulating the desired engine torque and desired wheel torque [42] or a throttle pedal that caused the vehicle to decelerate more strongly upon releasing the pedal in critical car-following situations [43]. 1.3. Aim and Hypotheses Little is known about how drivers perceive and respond to vehicle parameter adjustments that intend to provide a sporty driving experience for electric vehicles, such as MTM and AES technology. It is important to investigate this topic with a view to road safety. If such systems reduce vehicle controllability and increase driving speed, this could be seen as undesirable. The current study aimed to investigate how drivers perceive and respond to AES and MTM, two systems that intend to provide a sporty driving experience for electric vehicles and do not change the vehicle’s performance envelope in any way. The individual and combined contributions of MTM and AES were compared to a Baseline condition and a vehicle that offered increased engine power (“Sports car”). The Sports car was included to investigate how the results for AES and MTM compare to a car that offers actually increased sportiness. The combined condition (AES-MTM) was included to examine whether or not the effects of MTM and AES are additive. The expected effects of MTM and AES can be explained using theory from the field of manual control (e.g., [44]). Figure 1 shows a model of human driving behavior in a speed control task, based on Weir and Chao [45] and McRuer et al. [46]. Here, the human outputs a foot movement (“throttle driver”), which via the throttle mapping (a variable gain, i.e., a multiplication factor) results in an input to the vehicle model (“throttle engine,” describing how much torque is requested from the car). The car model outputs the current driving speed, which is fed back to the driver via visual and auditory pathways. The driver perceives these two feedback sources with a time delay. Additionally, the driver is represented by a gain, which describes how strongly the driver responds to the difference between the perceived speed and the desired speed. The desired speed represents the speed at which the driver wishes to drive at a particular moment; it is dependent on many factors, including the environment (road curvature; road width), the driver’s personality, and the driver’s risk assessment based on the visual and auditory information received.
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Jaywalking is a dangerous illegal crossing behavior, which is common but difficult to govern. If road is a kind of public goods, jaywalking can be regarded as a completely uncooperative behavior in the game of the public goods. Jaywalking is a relatively broad concept, which includes both the behaviors of pedestrians running a red light at the crosswalks and the behaviors of pedestrians directly crossing the road regardless of the traffic rules outside the crosswalks. At the same time, the traffic signal settings in the road network include both the signal lights at the intersections and the signal lights in the mid-roadways. The jaywalking behavior outside the crosswalks in the mid-roadways may be more common and difficult to govern due to the lack of management. Therefore, this study aims at the governance of jaywalking behaviors outside the crosswalks in the mid-roadways. Clearly, time pressure has a huge impact on crossing decision-making processes of pedestrians, and even causes pedestrians to fall into a jaywalking dilemma. The points decision-making guidance mechanism (PDGM) established by us is proposed based on a crossing decision-making model and a points guidance system in this problem-oriented context, which takes time pressure as an important factor. The points rules that the points guidance system relies on mainly apply the Elo algorithm to realize the automatic update of the points. The decision-making processes of pedestrians crossing the road can be positively influenced by the PDGM, which adopts a multi-factor crossing decision-making model. Since our PDGM is an innovative soft strategy based on the development of future intelligent transportation systems, which has not yet been established, so the introduction of a simulation modeling method is very necessary. In particular, this paper establishes an agent-based model to describe the effects of various aspects of the PGDM, and further verifies its effectiveness and feasibility with quantitative methods. The PDGM is beneficial to reduce jaywalking behaviors of pedestrians and effectively promote cooperation between pedestrians and traffic rules. The establishment of the PDGM has been proved to be feasible and effective, which can significantly promote legal crossing and improve the level of crossing safety without affecting the operating efficiency of vehicles by a simulation modeling method. The PDGM, which achieves a good performance, can be pioneered and applied to induce pedestrians to cross the road cooperatively and legally. What's more, the PDGM has been proved to be effective and has great potential to be applied to more traffic scenarios. In summary, it is conducive to urban traffic road more efficient and safer through the study of the points guidance mechanism for pedestrians.
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The integration of electric vehicles (EVs) will affect both electricity and transport systems and research is needed on finding possible ways to make a smooth transition to the electrification of the road transport. To fully understand the EV integration consequences, the behaviour of the EV drivers and its impact on these two systems should be studied. This paper describes an integrated simulation-based approach, modelling the EV and its interactions in both road transport and electric power systems. The main components of both systems have been considered, and the EV driver behaviour was modelled using a multi-agent simulation platform. Considering a fleet of 1000EV agents, two behavioural profiles were studied (Unaware/Aware) to model EV driver behaviour. The two behavioural profiles represent the EV driver in different stages of EV adoption starting with Unaware EV drivers when the public acceptance of EVs is limited, and developing to Aware EV drivers as the electrification of road transport is promoted in an overall context. The EV agents were modelled to follow a realistic activity-based trip pattern, and the impact of EV driver behaviour was simulated on a road transport and electricity grid. It was found that the EV agents’ behaviour has direct and indirect impact on both the road transport network and the electricity grid, affecting the traffic of the roads, the stress of the distribution network and the utilization of the charging infrastructure.
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This paper examines participants who are blind, with and without normal hearing, regarding detection of forward approaching and backing vehicles operating in electric mode (identified in this paper as quiet vehicles) under low speed conditions. Testing under low ambient sound conditions involved evaluation of internal combustion engine vehicles, hybrid vehicles operating in electric mode (EM), and the same hybrid vehicles operating in EM but with five different artificially generated sounds. Three of the five artificial sounds improved detection relative to the internal combustion engine condition for both forward and backward detection tasks. Regression analysis indicated that significant predictors of forward detection performance include average wind speed, amplitude modulation of the signal, hearing loss in the 500 Hz range, vehicle velocity, minimum ambient sound level, and overall vehicle sound level in units of A-weighted decibels. The corresponding analysis for backward detection indicated significant predictors, including a hearing loss in the 4 or 500 Hz ranges, vehicle velocity, minimum ambient sound level, vehicle sound level at the 250 Hz octave band, and the overall vehicle sound level in A-weighted decibels. DOI: 10.1061/(ASCE)TE.1943-5436.0000478. (C) 2013 American Society of Civil Engineers.
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Parking choice is an essential part of individual transportation; however, many travel demand and traffic simulations do not include parking. This paper reports on a proposal for a simple parking model and describes how this model was implemented into an existing, agent-based traffic simulation. The parking model provides feedback to the traffic simulation so that the overall simulation can react to spatial differences in parking demand and supply. Simulation results of a scenario in the city of Zurich, Switzerland, demonstrated that the model could capture key elements of parking, including capacity and pricing, and could assist with designing parking-focused transport policies. The paper also discusses possible work, such as microsimulation of the search for large-scale parking.
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The present paper represents an approach to the modeling of pedestrians and vehicles interaction in the area of a zebra crossing, either signalised or not, employing two existing models devoted to the simulation of the specific pedestrian and vehicular sub-systems and integrating them in a comprehensive agent environment. The latter acts as a bridge allowing mutual perception of the different heterogeneous agents that cooperate to avoid accidents: vehicles give way to perceived pedestrians whenever they can safely brake to let them pass, pedestrians yield whenever they perceive cars that would not be able to stop before the zebra crossing. The paper presents the model and shows results in simple crossing scenarios.
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Giving pedestrians priority to cross a street enhances pedestrian life, especially if crosswalks are closely spaced. Explored here is the effect of this management decision on car traffic. Since queuing theory suggests that for a given pedestrian flux the closer the crosswalk spacing the lower the effect of pedestrians on cars, scenarios where pedestrians can cross anywhere should be best for both cars and pedestrians. This is the kind of pedestrianization studied. Analytic formulas are proposed for a pedestrianized street’s capacity, free-flow speed and macroscopic fundamental diagram. Of these, only the free-flow speed formula is exact. The analytic form of the capacity formula is inspired by analytic upper and lower bounds derived with variational theory for a version of the problem where cars are treated as a fluid. The formula is then calibrated against microscopic simulations with discrete cars. The MFD for the fluid version of the problem is shown to be concave and have a certain symmetry. These two geometrical properties, together with the formulae for capacity and free-flow speed, yield a simple approximation for the MFD. Both the capacity and MFD formulae match simulations with discrete cars well for all values of the pedestrian flux – errors for the capacity are well under 0.2% of the capacity before pedestrianization. Qualitatively, the formulas predict that the street’s capacity is inversely proportional to the square root of the pedestrian flux for low pedestrian fluxes, and that pedestrians increase the cars’ free-flow pace by an amount that is proportional to the pedestrian flux.
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Explaining in detail how new e-mobility technologies work, and the system requirements which must be fulfilled for these new technologies to be implemented, this book augments this analysis with discussion of the business models, financing and social and economic conditions that will foster the emergence of a new e-mobility industry. New e-mobility technologies and business models will initiate changes in work patterns and in our personal choices on transportation means. This book looks at how smart cities may apply the "internet of things" to the transportation environment and how this may create a complete set of new technologies and service offerings that will enable the advent of the unmanned vehicle society. This e-mobility revolution will disrupt the transport market and bring opportunities and threats for many potential actors. These consequences are analysed within. This book is suitable for anyone interested in the e-mobility revolution and its impact on the future of cars, buses and trains.
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Electric Vehicles are very quiet at low speeds therefore people (especially the visually impaired) have difficulty recognizing that these vehicles are approaching. To address this concern, Approaching Vehicle Sound for Pedestrians system development has been discussed worldwide. In Japan, USA, Europe and China, government regulation is currently under study. As a solution to meet this concern, Nissan has developed the VSP (Approaching Vehicle Sound for Pedestrians) system for implementation on Nissan's first mass production Electric Vehicle. Nissan VSP emits a futuristic sound to satisfy 3 key stakeholders' concerns; for pedestrians to provide detectability, for drivers and neighborhoods to maintain a quiet environment. The sound emitted during forward motion has a "twin peaks and one dip" frequency signature, with modulation (or rhythmic structure) to accommodate human-beings ear frequency sensitivity, hearing loss due to aging and ambient noise conditions. Additionally, special emphasis is placed on the forward sound emitted when the vehicle is "taking-off'(starting forward motion)" to notify pedestrians that the vehicle is about to move, in response to real world feedback gathered in surveys with visually impaired in Japan and USA. The system also includes a reverse motion or "backing up" sound that has an easy to recognize cadenced(or rhythmic structure) characteristic.
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In this paper, an agent-based model is developed to study the market share evolution of passenger vehicles in Iceland, a country rich in domestic renewable energy. The model takes into account internal combustion engine (ICE) vehicles that are currently dominant in the market and electric vehicles (EVs) that are likely to enter the market in the future. The vehicles compete for market penetration through a vehicle choice algorithm that accounts for social influences and consumers' attractiveness for vehicle attributes. The main result provided by the modeling approach is the market share of different vehicles during the time period 2012–2030. The effects of fuel prices, vehicle taxes, future price of EVs and recharging concerns on the market share are analyzed with the help of the model. The results show that EVs would seize the market completely in the scenario combined of high gasoline price, decreasing EV price without tax and no worry about the recharging of EVs. The successful penetration of EVs in the scenarios with low gasoline price and combination of medium gasoline price and constant EV price needs policy supports like tax exemption and an environment where consumers do not suffer from range anxiety.
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In many Chinese cities, pedestrian’s road crossing behavior is different from that of pedestrians in developed countries. This paper presents a pedestrian model for traffic system micro-simulation in China. Considering the high rate of signal non-compliance, we classify pedestrians into two types: law-obeying ones and opportunistic ones. Opportunistic ones decide whether to violate traffic signal during red man, depending on the states of some external factors (like policeman, vehicle flow and other pedestrians’ behaviors). Questionnaires were used to determine the proportions of these two types of pedestrians under different circumstances. In addition, a time gap distribution extracted from videotape were used to determine the criterion for pedestrians to decide whether to walk or wait when they conflict with vehicle flows. However, simulation results deviate from the data extracted from videotape in some degree. By adjusting the parameters on the basis of analyzing the occurrence of the deviations, the simulation results agree with the field results better. This model has represented the high rate of pedestrians’ red light running and the mixed characteristics of traffic flows in Chinese cities, and it may be applicable in the micro-simulation of traffic system in other developing cities.
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This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.
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International accident statistics indicate that elderly pedestrians make up an extremely vulnerable road-user group. Past research has shown that older adults make many unsafe street-crossing decisions and adopt insufficient safety margins, especially when vehicles are approaching at high speed. Apart from studies on road design and speed-limit countermeasures, there is surprisingly no road-safety research on behavior-based measures to improve older pedestrians' safety. In this line, the present study was aimed at (i) assessing the effectiveness of a training program for older pedestrians that combined behavioral and educational interventions, and (ii) examining whether and to what extent age-related differences in street-crossing safety could be reduced after training older adults. Twenty seniors were enrolled in a training program. Before, immediately after, and six months after training, street-crossing behavior was assessed using a simulated street-crossing task. Twenty younger participants performed the same simulated task to obtain a baseline measure. The results showed that the training produced significant short- and long-term benefits, due to a shifting of the decision criteria among the older participants towards more conservative judgments. When compared with the younger group, the older participants improved their behavior considerably so that significant differences in the mean safety-related indicators were no longer observed. However, the older participants' ability to take the oncoming car's speed into account did not improve. Even after training, and contrary to younger adults, older participants were found to make more and more unsafe decisions as the car's speed increased, putting them at a higher risk at high speeds. This finding may reflect age-related perceptual and cognitive difficulties that cannot be remedied by a behavioral or educational training method. The present findings underline that high speed is an important risk factor for elderly pedestrians that should be handled by effective speed reduction measures (i.e. speed ramps, road narrowing).
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Based on an interactive road-crossing task, this study examined age-related effects on crossing decisions and whether or not age affects behavioral adjustments to the time gap. It also compared crossing-task decisions to previously observed estimation-task decisions [Lobjois, R., Cavallo, V., 2007. Age-related differences in street-crossing decisions: the effects of vehicle speed and time constraints on gap selection in an estimation task. Accident Analysis and Prevention 39 (5), 934-943]. The results showed that older adults selected a greater mean time gap and initiated their crossing sooner than the younger ones, indicating an attempt to compensate for their increased crossing time. However, older adults accepted shorter and shorter time gaps as speed increased, putting them at a higher risk at high speeds. Regarding adaptive behavior, the analyses showed that all groups adjusted their crossing time to the available time. Comparison of crossing decisions and estimations revealed that the young group had a greater number of tight fits and missed fewer opportunities on the crossing task, whereas these differences did not appear in the elderly. This suggests that the crossing decisions of younger adults are much more finely tuned to time gaps in actual crossing tasks than in estimation tasks and that older adults have trouble calibrating perception and action and perceiving possibilities for action.
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There is evidence linking certain vehicle characteristics to crash involvement and one possible mechanism behind this relationship is that these vehicle characteristics influence drivers' risk-taking behaviour. In order to investigate this, we conducted a roadside observation survey and a questionnaire-based study. Both revealed a significant relationship between vehicle performance and drivers' risk-taking behaviour. The causal direction of this relationship has important consequences. If drivers' risk taking predicts their car choice, then it could be justifiably argued that individuals who take more risks when driving simply choose more powerful vehicles to facilitate their behaviour. However, if it is the case that vehicle characteristics adversely influence drivers' risk-taking propensity then this has implications for vehicle design. Results indicated that the causal pathway operates independently in both directions. Finally, we sought to determine which vehicle characteristics influenced risk-taking intentions independently of other confounded characteristics. We found that high vehicle performance and a greater number of safety features led independently to greater intended risk taking in general, while higher internal car noise led to closer car following and more risky gap acceptance, but not to greater speed. Vehicle smoothness and handling did not affect risk-taking intentions.
An Analysis of Hybrid and Electric Vehicle Crashes in the
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Quieter Cars and the Safety of Blind Pedestrians, Phase 2: Development of Potential Specifications for Vehicle Countermeasure Sounds Final Report
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