Article

Pedestrians' road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling

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Abstract

The objective of this research is to explore the relation between personal characteristics of pedestrians and their crossing behaviour in front of an automated vehicle (AV). For this purpose, a simulation experiment was developed using Agent-Based Modelling (ABM) techniques. Sixty participants were asked to cross the road in a virtual environment displayed on a computer screen, allowing to record their crossing behaviour when in the presence of AVs and conventional vehicles (CVs). In some experimental configurations, the AVs communicated their intention to continue or not to continue their trajectories through the use of lights. The ABM allowed controlling the behaviour of the vehicles when interacting with the simulated avatar of the respondents. The subjects of the experiment were also asked to fill in a questionnaire about usual behaviour in traffic, as well as attitudes and risk perceptions toward crossing roads. The questionnaire data were used to estimate individual specific behavioural latent variables by means of principal component analysis which resulted in three main factors named: violations, lapses, and trust in AVs. The results of generalized linear mixed models applied to the data showed that besides the distance from the approaching vehicle and existence of a zebra crossing, pedestrians' crossing decisions are significantly affected by the participants' age, familiarity with AVs, the communication between the AV and the pedestrian, and whether the approaching vehicle is an AV. Moreover, the introduction of the latent factors as explanatory variables into the regression models indicated that individual specific characteristics like willingness to take risks and violate traffic rules, and trust in AVs can have additional explanatory power in the crossing decisions.

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... These findings correspond with Rothenbücher et al. (2016) who reported that respondents mentioned an uncertainty about the automated vehicle's behavior in a crossing decision, and with Pillai (2017) who revealed that respondents had difficulties to cross the road in front of an automated vehicle in a virtual-reality environment due to the lack of confirmation from the driver. Rad et al. (2020) found that respondents who trust in automated vehicle technology were more likely to cross the road before the automated vehicle. ...
... The literature widely supports the use of eye contact between pedestrians and human drivers as communication cue (Chang et al., 2016;Guéguen et al., 2015;Ren et al., 2016;Ŝucha, 2014). Recent studies (AlAdawy et al., 2019;Rad et al., 2020), however, propose that instead of making eye contact with an approaching vehicle when making their crossing decisions, pedestrians might be more likely to rely on vehicle kinematics (e.g., vehicle speed, distance to approaching vehicle, turn signals, brake lights) (Fridman et al., 2017;Ackermann et al., 2019). We encourage future research to examine the hypothesis that pedestrians look in the direction of the car approaching, and force the car to slow down without necessarily establishing eye contact with the driver. ...
... Similarly, Madigan et al. (2019) who analysed video data on the interactions between automated shuttles and external road users found incidences of road users testing an automated shuttle. In the study of Rad et al. (2020), 81.7% of respondents expected the automated vehicle to stop for them at zebra crossings, and even 25% of respondents everywhere. Manufacturers, vehicle operators, and the media should educate the public that the testing of automated vehicles could jeopardize the safety and efficiency benefits of vehicle automation. ...
Preprint
A necessary condition for the effective integration of automated vehicles in our daily lives is their acceptance by passengers inside and pedestrians and cyclists outside the automated vehicle. 119 respondents experienced an automated shuttle ride with a ‘hidden steward on board’ in a mixed traffic environment in Berlin-Schöneberg. A mixed-method approach was applied gathering qualitative interview data during the ride and quantitative questionnaire data after the ride. Responses were classified into three main categories: (1) Perceived safety, (2) interactions with automated shuttles in crossing situations, and (3) communication with automated shuttles. Respondents associated their perceptions of safety with the low speed, dynamic object and event identification, longitudinal and lateral control, pressing the emergency button inside the shuttle, their general trust in technology, sharing the shuttle with fellow travellers, the operation of the shuttle in a controlled environment, and the behaviour of other road users outside the shuttle. Respondents pressed the emergency button inside the automated shuttle on 28 out of 62 test rides in order to test its behavior. They further expected to be more cautious in crossing the road before an automated shuttle due to the lack of eye contact with the human driver and a lack of trust in the behavior of the automated shuttle, and expected road users testing the automated shuttle due to the conservative driving behavior of automated shuttles. We recommend future research into the hypothesis that the acceptance of automated shuttles will be associated with the perceived safety of and their effective and intuitive interaction and communication with both passengers and other road users.
... ey constructed a 3D model of the building and analyzed the evacuation pattern under two different evacuation plans to decide which one is better. Another recent application of ABM in pedestrian dynamics is evaluating the interaction between pedestrians and automated vehicles (AVs) [48]. e authors examined the effect of pedestrians' characteristics in crossing the road in front of AVs under different circumstances. ...
... Most of the applications of ABM in marketing is related to the diffusion of information. Many researchers have integrated ABM and network theory for modeling the diffusion of information [12,48,49]. A comprehensive literature review about the applications of ABM in marketing was provided in [16]. ...
... In this way, the AB model will be built at first, and the model will be examined under the scenarios that the decision maker wants to test. An AB model for simulating the road crossing behavior of pedestrians has been developed in [48]. After model development, the AB model has been used for examining the behavior of a pedestrian in crossing a road in different conditions. ...
Article
Full-text available
Making proper decisions in today’s complex world is a challenging task for decision makers. A promising approach that can support decision makers to have a better understanding of complex systems is agent-based modeling (ABM). ABM has been developing during the last few decades as a methodology with many different applications and has enabled a better description of the dynamics of complex systems. However, the prescriptive facet of these applications is rarely portrayed. Adding a prescriptive decision-making (DM) aspect to ABM can support the decision makers in making better or, in some cases, optimized decisions for the complex problems as well as explaining the investigated phenomena. In this paper, first, the literature of DM with ABM is inquired and classified based on the methods of integration. Performing a scientometric analysis on the relevant literature lets us conclude that the number of publications attempting to integrate DM and ABM has not grown during the last two decades, while analysis of the current methodologies for integrating DM and ABM indicates that they have serious drawbacks. In this regard, a novel nature-inspired model articulation called optimal agent framework (OAF) has been proposed to ameliorate the disadvantages and enhance the realization of proper decisions in ABM at a relatively low computational cost. The framework is examined with the Bass diffusion model. The results of the simulation for the customized model developed by OAF have verified the feasibility of the framework. Moreover, sensitivity analyses on different agent populations, network structures, and marketing strategies have depicted the great potential of OAF to find the optimal strategies in various stochastic and unconventional conditions which have not been addressed prior to the implementation of the framework.
... These findings correspond with Rothenbücher et al. (2016) who reported that respondents mentioned an uncertainty about the automated vehicle's behavior in a crossing decision, and with Pillai (2017) who revealed that respondents had difficulties to cross the road in front of an automated vehicle in a virtual-reality environment due to the lack of confirmation from the driver. Rad et al. (2020) found that respondents who trust in automated vehicle technology were more likely to cross the road before the automated vehicle. ...
... The literature widely supports the use of eye contact between pedestrians and human drivers as communication cue (Chang et al., 2016;Guéguen et al., 2015;Ren et al., 2016;Ŝucha, 2014). Recent studies (AlAdawy et al., 2019;Rad et al., 2020), however, propose that instead of making eye contact with an approaching vehicle when making their crossing decisions, pedestrians might be more likely to rely on vehicle kinematics (e.g., vehicle speed, distance to approaching vehicle, turn signals, brake lights) (Fridman et al., 2017;Ackermann et al., 2019). We encourage future research to examine the hypothesis that pedestrians look in the direction of the car approaching, and force the car to slow down without necessarily establishing eye contact with the driver. ...
... Similarly, Madigan et al. (2019) who analysed video data on the interactions between automated shuttles and external road users found incidences of road users testing an automated shuttle. In the study of Rad et al. (2020), 81.7% of respondents expected the automated vehicle to stop for them at zebra crossings, and even 25% of respondents everywhere. Manufacturers, vehicle operators, and the media should educate the public that the testing of automated vehicles could jeopardize the safety and efficiency benefits of vehicle automation. ...
Article
Full-text available
A necessary condition for the effective integration of automated vehicles in our daily lives is their acceptance by passengers inside and pedestrians and cyclists outside the automated vehicle. 119 respondents experienced an automated shuttle ride with a ‘hidden steward on board’ in a mixed traffic environment in Berlin-Schöneberg. A mixed-method approach was applied gathering qualitative interview data during the ride and quantitative questionnaire data after the ride. Responses were classified into three main categories: (1) Perceived safety, (2) interactions with automated shuttles in crossing situations, and (3) communication with automated shuttles. Respondents associated their perceptions of safety with the low speed, dynamic object and event identification, longitudinal and lateral control, pressing the emergency button inside the shuttle, their general trust in technology, sharing the shuttle with fellow travellers, the operation of the shuttle in a controlled environment, and the behaviour of other road users outside the shuttle. Respondents pressed the emergency button inside the automated shuttle on 28 out of 62 test rides in order to test its behavior. They further expected to be more cautious in crossing the road before an automated shuttle due to the lack of eye contact with the human driver and a lack of trust in the behavior of the automated shuttle, and expected road users testing the automated shuttle due to the conservative driving behavior of automated shuttles. We recommend future research into the hypothesis that the acceptance of automated shuttles will be associated with the perceived safety of and their effective and intuitive interaction and communication with both passengers and other road users.
... Another critical point during the pedestrian crossing is the presence or not of a pedestrian crossing that increases pedestrian's intention to cross the street (Clamann et al., 2017;Jayaraman et al., 2018;Razmi Rad et al., 2020;Schieben et al., 2019;Velasco et al., 2019). For example, Clamann et al. (2017) observed that 56% of pedestrians cross after the vehicle without a pedestrian crossing. ...
... Indeed, some studies used videos (e.g., Ackermann et al., 2019b;de Clercq et al., 2019;Dey et al., 2019a), virtual reality (e.g., Chang et al., 2017;Jayaraman et al., 2018;Löcken et al., 2019;Velasco et al., 2019), questionnaires (e.g., Schieben et al., 2019) or Wizard-of-Oz vehicle in a natural setting (e.g., Clamann et al., 2017;Dey et al., 2020;Mahadevan et al., 2018;Rothenbücher et al., 2016). If each method has its advantages and disadvantages, some studies have shown the efficiency of the use of the virtual environment to test both the behavior and the users' experience (e.g., Deb et al., 2018;Holländer et al., 2019;Razmi Rad et al., 2020). For example, in the study of Razmi Rad et al. (2020), pedestrians are confronted with conventional and autonomous vehicles with and without eHMI in a virtual environment and asked about their interactions with AVs (e.g., perception of eHMI, elements of decision). ...
... If each method has its advantages and disadvantages, some studies have shown the efficiency of the use of the virtual environment to test both the behavior and the users' experience (e.g., Deb et al., 2018;Holländer et al., 2019;Razmi Rad et al., 2020). For example, in the study of Razmi Rad et al. (2020), pedestrians are confronted with conventional and autonomous vehicles with and without eHMI in a virtual environment and asked about their interactions with AVs (e.g., perception of eHMI, elements of decision). Their results have shown different road crossing behavior according to the type of vehicle and different variables (e.g., type of infrastructure, distance from the vehicle) and a reported assessment of the importance of a communication system for the AVs. ...
Article
The number of studies on autonomous vehicles has increased over recent years. Many of these studies have indicated the importance of an external Human-Machine Interface of communication (eHMI) on autonomous vehicles to indicate their intentions to other road users. Using an experimental design, we compared three eHMIs coupled to three road infrastructures to observe pedestrians' crossing behavior and collect their feelings about different vehicle types. Our results showed that the eHMIs influence the pedestrians' decision to cross the street, confirming the importance of setting up eHMIs. The proportion of pedestrians who crossed in front of the autonomous vehicles was more significant for vehicles equipped with an eHMI than vehicles without an eHMI. In 10% of cases, pedestrians used circumvention strategies rather than crossing in front of a vehicle without an eHMI. This behavior was more often observed when there was no protected infrastructure. Finally, while our objective data failed to indicate whether a specific eHMI is better accepted than another, the subjective data on the participants' preferences provided some promising ideas for further studies and the eHMI final implementation.
... On the other hand, vehicles may also behave differently than expected or be misunderstood by pedestrians and other road users. All of this can lead to unsafe situations [223][224][225]. ...
... Therefore, one of the huge challenges for the implementation of AVs is how to replicate these implicit and explicit communication strategies in the absence of person-to-person communication [224,266]. The importance of this issue is highlighted by recent studies that revealed that AV's dedicated communication with VRU could help them overcome their fear of AV [223,267,268]. Indeed, in close-proximity situations AVs, the lack of "traditional" communication between VRU and vehicles can lead to frustrations for VRUs, increasing the degree of conflict and creating unpleasant and unsafe interactions, [168,210,266,269,270]. ...
... Recent results of generalized linear mixed models showed that pedestrians' crossing decisions are significantly affected by the participants' age. They found that the younger group (age < 40) was more willing to cross the road when all vehicles were automated [223]. Likewise, Madigan et al. [168] showed that the types of interaction varied considerably across sociodemographic groups, with females and older users more likely to show cautionary behavior around the AVs than males, or younger road users. ...
Chapter
The most frequent justification for implementing automated vehicles is the claim that they will increase road safety by removing human involvement in driving. This, however, introduces emerging Human Factors (HFs) issues, since regardless of the level of automation, the human being will continue to play a crucial role in interacting with vehicle automation. In the medium-low levels, the driver will have to play a supervisory role which will introduce out-of-the-loop problems, in the driver-vehicle interaction during the transition of control. At the higher level, new forms of accidents may occur associated with the need for automated vehicles to interact with other road users. The chapter is a thorough literature review of the HFs for both of these interactions, mainly those relating to the medium-low level of automation. Such review is aimed at understanding the influences of HFs on road safety and the role played by infrastructures.
... Recently, there has been emergence of literature which has examined pedestrian-AV interaction (see Ezzati Amini et al., 2021), particularly studies which have investigated pedestrians' intentions to cross in front of AVs (e.g., Epke et al., 2021;Jayaraman et al., 2020;Nuñez Velasco et al., 2019;Palmeiro et al., 2018;Rad et al., 2020). For example, Nuñez Velasco et al (2019) examined 55 participants intentions to cross infront of an automated passenger shuttle (WEpod). ...
... Further, higher self-report ratings of perceived behavioural control (PBC; perceived ease or difficulty of performing a behaviour) and trust were also associated with higher crossing intentions. Similarly, Rad et al. (2020) reported that the presence of a zebra crossing and distance from the AV were significant predictors of crossing behaviour. Further, participants aged between 18 and 40 years crossed nearly twice as much more in front of the approaching AV than participated aged over 40 years. ...
... As outlined in the introduction, variables including environment and vehicle factors (e.g., presence of a zebra crossing, the distance between pedestrian and vehicle, speed of vehicle), demographic factors (e.g., age), and external human-machine interfaces (e.g., textual, visual eHMIs) may also influence pedestrian crossing intentions (e.g., Bazilinskyy et al., 2019;Nuñez Velasco et al., 2019;Rad et al., 2020;Wang et al., 2021). eHMIs are designed to communicate the intentions of the AV to those outside of the vehicle (e.g., pedestrians, bicyclists). ...
Article
Theory of planned behaviour Technology acceptance model Unified theory of acceptance and use of technology A B S T R A C T Adoption of Automated Vehicles (AVs) within transport networks relies on the technology acceptance of not only AV users, but also other road users such as pedestrians. However, previous research has mostly focused on user acceptance of AVs and the receptivity of pedestrians towards AVs has been largely unexplored. This study aims to fill this gap by applying the Theory of Planned Behaviour (TPB), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate pedestrians' intentions to cross a road in front of a fully AV. To achieve this goal, a 20-minute online questionnaire was administered in Australia and data were collected from a total of 485 participants (average age = 35.35 years, 51.5% female). Bivariate correlation analysis and hierarchical regression models were then applied on the data to investigate the association between pedestrian attributes and their behavioural intentions. The findings revealed that the TPB and the UTAUT explained 46% and 43% of the variance in intentions to cross a road in front of a fully AV, respectively, with perceived behavioural control (PBC) and subjective/social norms the most significant unique predictors of intentions within the TPB and UTAUT, respectively. The TAM, however, only explained 35% of the variance in intentions to cross a road in front of a fully AV. When added into Step 2 of the hierarchical regression, age accounted for additional variance above the TAM predictors, indicating that younger participants reported higher intentions to cross a road in front of a fully AV than older participants. Age was not a significant predictor of intentions when entered with the predictors of the TPB and UTAUT. This study provides support for the use of these theoretical models to understand pedestrians' acceptance of AVs.
... In accordance with modern technology and the development of traffic systems, there is a need to explore the relation between personal characteristics of pedestrians and their crossing behaviour in front of an automated vehicle (AV). e results of generalized linear mixed models showed Mathematical Problems in Engineering 3 that besides the distance from the approaching vehicle and existence of a zebra crossing, pedestrians' crossing decisions are significantly affected by the participants' age, familiarity with AVs, the communication between the AV and the pedestrian, and whether the approaching vehicle is an AV [30]. In another study, the game theory is used to analyse the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. ...
... Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods [31]. e review of used literature and their contributions are summarized in Table 1 [9][10][11][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. ...
... Since the orientation of the criteria was taken into account when evaluating DMUs by all parameters using the linguistic scale, it means that all criteria were marked as benefit further in applying the fuzzy MARCOS method and (30) and (31) were applied to extend the initial fuzzy matrix. ...
Article
Full-text available
Pedestrians as a vulnerable category of traffic participants demand a special attention, particularly regarding their behavior at unsignalized pedestrian crossings. Unquestionably, when crossing a road at these types of pedestrian crossings, there is a potential risk, for both the pedestrians and other traffic participants, as well. Accordingly, this article shows the research on pedestrians’ behavior at unsignalized intersections, conducted at four locations in the urban environment of Novi Sad. The main goals of this study are reflected in developing a multiphase model by integrating different approaches into one original unique model. First, the efficiency of the observed locations of pedestrian crossings was determined by applying a model consisting of DEA (Data Envelopment Analysis), fuzzy DEA, entropy, CRITIC (CRiteria Importance Through Intercriteria Correlation), fuzzy FUCOM (Full Consistency Method), fuzzy PIPRECIA (PIvot Pairwise RElative Import Criteria Assessment), and fuzzy MARCOS (Measurement of alternatives and ranking according to COmpromise solution). Then, the following aim of this study is to determine the values of the critical interval and then to compare these values with the accepted interval, which can be considered one of the criteria of safe pedestrians’ crossing the roadway. Apart from this, the aim is related to determining the characteristics of pedestrians’ behavior at unsignalized crossings, with a special reference to gender differences, as well to the fact whether the pedestrian crosses the roadway as an individual or within a group. After the empirical research and data classification, efficiency calculation, an extensive statistical and verification analysis was conducted to determine the set goals. The results imply that the relationship of the values of the accepted and critical intervals indicates the occurrence of the risky behavior of a certain number of pedestrians, which is reflected in accepting the intervals that are not completely safe for crossing the roadway and which can negatively affect the sustainable functioning of the traffic system.
... Indeed, Jayaraman et al. (2018) found that a high level of trust was associated with the increased time that pedestrians spent jaywalking by AVs. Similarly, Rad et al. (2020) also found that pedestrians who trusted AVs tended to make their crossing decisions before AVs came to a full stop. It is therefore important to assess pedestrians' level of trust in AVs vs. HDVs in different road crossing contexts and the extent to which it explains pedestrians' risk-taking intention when interacting with those vehicles. ...
... In our study, participants reported higher trust in AVs both in terms of perceived predictability and reliability. Previous studies have shown that pedestrians with a higher level of trust in AVs tend to have a stronger willingness to cross in front of them (Nuñez Velasco et al., 2019;Rad et al., 2020). However, our study illustrates the risk that over-trusting AVs has on pedestrian behaviors. ...
Article
Due to the absence of a human driver, the introduction of fully automated vehicles (FAVs) may bring new safety challenges to the traffic system, especially when FAVs interact with vulnerable road users such as pedestrians. To ensure safer interactions between pedestrians and FAVs, this questionnaire-based study aims to understand Australian pedestrians’ intention to engage in risky road-crossing behaviors when they interact with FAVs vs. human-driven vehicles (HDVs). A 2 × 2 between-subject design was utilized, in which two risky road-crossing scenarios were designed and took into account the vehicle type (FAV vs. HDV) and vehicle speed (30 km/h vs. 50 km/h). A total of 493 participants (aged 18–77) were randomly assigned to one of the four experimental conditions and completed an online questionnaire based on the extended Theory of Planned Behavior (TPB). This questionnaire measured pedestrians’ intentions to cross the road in the assigned scenarios as well as the motivational factors behind these intentions in terms of attitude, subjective norm, perceived behavioral control, perceived risk and trust in the vehicle. The results show that pedestrians had significantly higher intentions to cross the road in front of approaching FAVs than HDVs. Participants also reported a lower risk perception of crossing in front of FAVs and greater trust in this type of vehicle. Attitude, subjective norm, and perceived behavioral control were significant predictors of intentions to engage in risky road-crossing behavior. Findings of this study provide important implications for the development and implementation of FAVs in the future road transport system.
... LMMs are able to consider random effects that cannot be controlled for in the experiment. Random effect models have been widely utilized for this purpose (Wang, Yang, & Hurwitz, 2019;Laird & Ware, 1982;Razmi Rad, Homem de Almeida Correia, & Hagenzieker, 2020). ...
Article
Connected and automated vehicles (CAVs) are expected to enhance traffic efficiency by driving at shorter time headways, and traffic safety by shorter reaction times. However, one of the main concerns regarding their deployment is the mixed traffic situation, in which CAVs and manually driven vehicles (MVs) share the same road. This study investigates the behavioural adaptation of MV drivers in car-following and lane changing behaviour when they drive next to a dedicated lane (DL) for CAVs and compares that to a mixed traffic situation. The expectation is that in a mixed traffic situation, the behavioural adaptation of MV drivers is negligible due to lower exposure time and scarce platoons, while concentrating the CAVs on one dedicated lane may cause significant behavioural adaptation of MV drivers due to a higher exposure time and conspicuity of CAV platoons. Fifty-one participants were asked to drive an MV on a 3-lane motorway in three different traffic scenarios, in a fixed-base driving simulator: (1) Base, only MVs were present in traffic, (2) Mixed, platoons of 2–3 CAVs driving on any lane and mixed with MVs, (3) DL, platoons of 2–3 CAVs driving only on a DL. The DL was recognizable by road signs and a buffer demarcation which separated the DL from the other lanes. A moderate penetration rate of 43% was assumed for CAVs. During the drives, the car following headways and the accepted merging gaps by participants were collected and used for comparisons of driving behaviour in different scenarios. Based on the results, we conclude that there is no significant difference in the driving behaviour between Base and Mixed scenarios at tested penetration rate, confirming our research expectation. However, in DL scenario, MV drivers drove closer to their leaders specially when driving on the middle lane next to the platoons and accepted shorter gaps (up to 12.7% shorter at on-ramps) in lane changing manoeuvres. Dedicating a lane to CAVs increases the density of CAV platoons on one lane and consequently their conspicuity becomes higher. As a result, MV drivers are influenced by CAV platoons on a DL and imitate their behaviour. The literature suggests that dedicating a lane to CAVs improves the traffic efficiency by providing more possibilities for platooning. This study shows that implementing such a solution will affect the driving behaviour of human drivers. This should be taken into consideration when evaluating the impacts of dedicated lanes on traffic efficiency and traffic safety.
... Actual street-crossing behavior in front of AVs among young and older adults has never been studied. Razmi Rad et al. (2020) recently made comparisons between young and older pedestrians using a virtual environment displayed on a computer screen, but their sample was largely made up of young male participants. Moreover, their participants could not actually cross the street but simply pressed a response button and an avatar walked automatically across the one-way street, in front of a single approaching car. ...
Article
Self-driving vehicles are gradually becoming a reality. But the consequences of introducing such automated vehicles (AVs) into current road traffic cannot be clearly foreseen yet, especially for pedestrian safety. The present study used virtual reality to examine the pedestrians’ crossing behavior in front of AVs as compared to conventional cars (CVs). Thirty young (ages 21–39) and 30 older (ages 68–81) adults participated in a simulated street-crossing experiment allowing for a real walk across an experimental two-way street. Participants had to cross (or not cross) in mixed traffic conditions where highly perceptible AVs always stopped to let them cross, while CVs did not brake to give them the right of way. Available time gap (from 1 to 5 s), approach speed (30 or 50 km/h), and the lane in which the cars were approaching (near and/or far lane of the two-way street) were varied. The results revealed a significantly higher propensity to cross the street, at shorter gaps, when AVs gave way to participants in the near lane while CVs were approaching in the far lane, leading to more collisions in this condition than in the others. These risky decisions were observed for both young and older participants, but much more so for the older ones. The results also indicated hesitation to cross in front of an AV in both lanes of the two-way street, with later initiations and longer crossing times, especially for the young participants and when the AVs were approaching at a short distance and braked suddenly. This study highlights the potential risks for pedestrians of introducing AVs into current road traffic, complicating the street-crossing task for young and older people alike. Future studies should look further into the role of repeated practice and trust in AVs. The design of these vehicles must also be addressed. Some practical recommendations are provided.
... However, study time, weather, traffic conditions, and strict ethical requirements limit the application of WoZ. According to immersion and interactivity, VR technology has been used in many studies [20,30]. However, VR headsets may cause discomfort or even dizziness and influence the crossing behavior of pedestrians. ...
Article
Full-text available
With the increasing number of automated vehicles (AVs) being tested and operating on roads, external Human–Machine Interfaces (eHMIs) are proposed to facilitate interactions between AVs and other road users. Considering the need to protect vulnerable road users, this paper addresses the issue by providing research evidence on various designs of eHMIs. Ninety participants took part in this experiment. Six sets of eHMI prototypes—Text, Arrowed (Dynamic), Text and Symbol, Symbol only, Tick and Cross and Traffic Lights, including two sub-designs (Cross and Do Not Cross)—were designed. The results showed that 65.1% of participants agreed that external communication would have a positive effect on pedestrians’ crossing decisions. Among all the prototypes, Text, and Text and Symbol, eHMIs were the most widely accepted. In particular, for elderly people and those unfamiliar with traffic rules, Text, and Text and Symbol, eHMIs would lead to faster comprehension. The results confirmed that 68.5% of participants would feel safer crossing if the eHMI had the following features: ‘Green’, ‘Text’, ‘Symbol’, or ‘Dynamic’. These features are suggested in the design of future systems. This research concluded that eHMIs have a positive effect on V2X communication and that textual eHMIs were clear to pedestrians.
... Furthermore, a video analysis revealed differences in road user behavior of older people when interacting with an AV, with older pedestrians (aged 55 years and above) stopping more often to give priority to the AV [34]. This result was supported by a simulation experiment in which older pedestrians (aged 40-69 years) were more hesitant about interacting with an AV when crossing a road [44]. ...
Conference Paper
Full-text available
To communicate perception of and intent to other road users, implicit and explicit forms of communication for automated vehicles (AVs) are currently under research and development. Despite being a relevant group for road safety, the requirements of elderly pedestrians are not sufficiently reflected in current communication concepts. Age-related impairments of sensory, cognitive and motor abilities of elderly pedestrians are presented and their relevance for design criteria of implicit and explicit forms of communication for AVs derived. The specification of design criteria presented in this paper allows further research to examine the design of implicit and explicit communication for AVs with elderly pedestrians.
... Simulating the pedestrians depicts their behaviors in different conditions, and to this aim, simulations should be verified, validated, and calibrated to assure the results (Rasouli, 2021). The pedestrian simulation literature has been conducted on three scales of agent-based: the most complicated simulation method that pedestrians are dynamically considered with reactions to their surroundings, making decisions, and navigations (Antonini et al., 2006;Brunnhuber et al., 2012;Hussein and Sayed, 2019;Rad et al., 2020;Rivers et al., 2014;Vizzari et al., 2015;Zhou et al., 2010); entity based: pedestrians are simulated individually which is appropriate for small and medium-sized crowds (Zhou et al., 2010); and flow-based: a whole pedestrian crowd simulation for dense crowds (Feng et al., 2013;Hughes, 2002;Wang et al., 2018). From methods perspective, pedestrian simulation studies have been used cellular automata models: considering the network of cells for pedestrians (Blue and Adler, 2001;1999;Hu et al., 2018;Li and Han, 2015;Li et al., 2020b), network-based models: assuming networks with nodes and links (Borgers and Timmermans, 1986;Hoogendoorn and Bovy, 2004;Yamashita et al., 2009), machine learning methods: such as Support Vector Machine, Neural Network, Decision Tree methods (Martin and Parisi, 2020;Song et al., 2018;Wang et al., 2019), and physics-based models: using physical forces to analyze and simulate the pedestrian's engagement (Corbetta et al., 2018;Dietrich and Köster, 2014;Jo et al., 2013). ...
Article
Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: “Pedestrian crash frequency models”, “Pedestrian injury severity crash models”, “Traffic engineering measures in pedestrians’ safety”, “Global reports around pedestrian accident epidemiology”, “Effect of age and gender on pedestrians’ behavior”, “Distraction of pedestrians”, and “Pedestrian crowd dynamics and evacuation”. Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely “Collision avoidance and intelligent transportation systems (ITS)”, “Epidemiological studies of pedestrian injury and prevention”, “Pedestrian road crossing and behavioral factors”, “Pedestrian flow simulation”, and “Walkable environment and pedestrian safety”. Finally, “autonomous vehicle”, “pedestrian detection”, and “collision avoidance” themes were identified as having the greatest centrality and development degrees in recent years.
... Few studies have attempted to model the behaviour of pedestrians in the presence of an AV aiming to contribute in increasing safety in these types of interactions. More specifically, Rad et al. (2020) modelled the probability for a pedestrian to cross the road based on the type of the vehicle (conventional or fully autonomous), the type of communication display, the kinematic characteristics of the two agents and demographic parameters using agent -based simulation modelling. An agentbased model was also developed by Prédhumeau et al. (2021) for assisting autonomous vehicles in predicting pedestrian trajectories in shared spaces. ...
Article
The advent of autonomous vehicles brings major changes in the transportation systems influencing the infrastructure design, the network performance, as well as driving functions and habits. The penetration rate of this new technology highly depends on the acceptance of the automated driving services and functions, as well as on their impacts on various traffic, user oriented and environmental aspects. This research aims to present a methodological framework aiming to facilitate the modelling of the behaviour of new AV driving systems and their impacts on traffic, safety and environment. This framework introduces a stepwise approach, which will be leveraged by stakeholders in order to evaluate the new technology and its components at the design or implementation phase in order to increase acceptance and favor the adoption of the new technology. The proposed framework consists of four sequential steps: i. conceptual design, ii. data collection, processing and mining, iii. modelling and iv. autonomous vehicles impact assessment. The connection between these steps is illustrated and various Key Performance Indicators are specified for each impact area. The paper ends with highlighting some conceptual and modeling challenges that may critically affect the study of acceptance of autonomous vehicles in future mobility scenarios.
... Studies were found investigating interactions between vulnerable road users (VRU, such as pedestrians, cyclists, motorcycles, and e-scooter drivers) and AV as well as with left turn interactions among MV on the basis of photo studies, questionnaires, the application of simulators, or virtual reality (e.g. Hagenzieker et al., 2020;Rad et al., 2020;Vlakveld et al., 2020). Zhou et al. (2014) analysed gap acceptance in conditionally tolerable left turn manoeuvres from the major road at unsignalised intersections in dependence on the number of rejected gaps and their duration adopting the generalised estimating equations approach. ...
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Highly and fully automated driving has been under development for the past two decades in order to increase comfort, efficiency, and traffic safety. Particularly in the latter domain, experts agree on automated driving, especially in case of automated vehicles (AV) with SAE level 4 or higher, having the most promising effects. Automated driving is expected to decrease the number of seriously injured or even killed road users to zero (Vision Zero). However, automated driving is still in an early stage of development and many AV tend to drive very carefully to avoid crashes. So, the goal is to make driving more efficient while maintaining the highest level of safety. In the project "Digitaler Knoten 4.0" cooperative automated driving was assessed regarding efficiency and safety aspects. One of the use cases investigated was turning left with oncoming traffic at an urban intersection as this situation represents one of the most complex situations in urban areas yielding to crashes within many cases-serious consequences for the involved road users. At the Application Platform Intelligent Mobility (AIM) Research Intersection in Braunschweig, Germany, an SAE level 3 AV was turning left interacting with oncoming manually driven vehicles (MV). The performance of the AV was compared to MV executing the same manoeuvre. The recorded video-based trajectories of the respective AV as well as MV were analysed regarding the influence of situational factors (e.g. position of the vehicle in the queue and gap acceptance) and kinematic factors (e.g. speed and acceleration) on traffic safety. The similarities and differences between this specific AV and MV were identified yielding insight for further developing algorithms for more efficient driving while maintaining the same traffic safety level. For instance, it appears that the AV shows a very conservative left turning behaviour leading to very safe PET distributions in comparison to left turning MV.
... While parking space may be freed up, our results suggest that this space would be filled up by taxis which are waiting or interacting with customers. Crossing the street in front of an automated vehicle may become a complex endeavour in itself (Razmi Rad et al., 2020); and overseeing potentially unpredictable behaviour of arriving and leaving vehicles may even increase this complexity. ...
Preprint
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Automated Mobility on Demand (AMoD) is a concept that has recently generated much discussion. In cases where large-scale adoption of an automated taxi service is anticipated, the service’s impacts may become relevant to key transport system metrics, and thus to transport planners and policy-makers as well. In light of this increasingly important question, this paper presents an agent-based transport simulation with (single passenger) AMoD. In contrast to earlier studies, all scenario data (including demand patterns, cost assumptions and customer behaviour) is obtained for one specific area, the city of Zurich, Switzerland. The simulation study fuses information from a detailed bottom-up cost analysis of mobility services in Switzerland, a specifically tailored Stated-Preferences survey about automated mobility services conducted in the canton of Zurich, and a detailed agent-based transport simulation for the city, based on MATSim. Methodologically, a comprehensive approach is presented that iteratively runs these components to derive states in which service cost, waiting times and demand are in equilibrium for a cost-covering AMoD operator with predefined fleet size. For Zurich, several cases are examined, with 4,000 AMoD vehicles leading to the maximum demand of around 150,000 requests per day that can be attracted by the system. Within these parameters, the simulation results show that customers are willing to accept average waiting times of around 4 minutes at a price of 0.75 CHF/km. Further cost-covering cases with lower demand are presented, where either smaller fleet sizes lead to higher waiting times, or larger fleet sizes lead to higher costs. While our simulations indicate that an AMoD system in Zurich can bring benefits to the users, they show that the system impact is largely negative. Caused by modal shifts, our simulations show an increase of driven distance of up to 100%. All examined fleet configurations of the unregulated, cost-covering, single-passenger, door-to-door AMoD service are found to be highly counter-productive on a path towards a more shared and active transport system. Accordingly, policy recommendations for regulation are discussed.
... In one experiment it was shown that when pedestrians were initially reacting to connected automated vehicles (CAVs), it was difficult for them to communicate with the CAVs. The interaction and communication between AVs and pedestrians affected crossing decisions [19]. Due to some miscommunications, initially with autonomous vehicles, it has caused accidents between the pedestrian and the vehicle. ...
Preprint
Full-text available
Level of emotional arousal of one's body changes in response to external stimuli in an environment. Given the risks involved while crossing streets, particularly at unsignalized mid-block crosswalks, one can expect a change in the stress level of pedestrians. In this study, we investigate the levels and changes in pedestrian stress, under different road crossing scenarios in immersive virtual reality. To measure the stress level of pedestrians, we used Galvanic Skin Response (GSR) sensors. To collect the required data for the model, Virtual Immersive Reality Environment (VIRE) tool is used, which enables us to measure participants' stress levels in a controlled environment. The results suggested that the density of vehicles has a positive effect, meaning as the density of vehicles increases, so does the stress level for pedestrians. It was noted that younger pedestrians have a lower amount of stress when crossing as compared to older pedestrians which have higher amounts of stress. Geometric variables have an impact on the stress level of pedestrians. The greater the number of lanes the greater the observed stress, which is due to the crossing distance increasing, while the walking speed remains the same.
... Studies were found investigating interactions between vulnerable road users (VRU, such as pedestrians, cyclists, motorcycles, and e-scooter drivers) and AV as well as with left turn interactions among MV on the basis of photo studies, questionnaires, the application of simulators, or virtual reality (e.g. Hagenzieker et al., 2020;Rad et al., 2020;Vlakveld et al., 2020). Zhou et al. (2014) analysed gap acceptance in conditionally tolerable left turn manoeuvres from the major road at unsignalised intersections in dependence on the number of rejected gaps and their duration adopting the generalised estimating equations approach. ...
Conference Paper
Full-text available
Highly and fully automated driving has been under development for the past two decades in order to increase comfort, efficiency, and traffic safety. Particularly in the latter domain, experts agree on automated driving, especially in case of automated vehicles (AV) with SAE level 4 or higher, having the most promising effects. Automated driving is expected to decrease the number of seriously injured or even killed road usersto zero (Vision Zero). However, automated driving is still in an early stage of development and many AV tend to drive very carefully to avoid crashes. So, the goal is to make driving more efficient while maintaining the highest level of safety. In the project Digitaler Knoten 4.0 cooperative automated driving was assessed regarding efficiency and safety aspects. One of the use cases investigated was turning left with oncoming traffic at an urban intersection as this situation represents one of the most complex situations in urban areas yielding to crashes with—in many cases—serious consequences for the involved road users. At the Application Platform Intelligent Mobility (AIM) Research Intersection in Braunschweig, Germany, an SAE level 3 AV was turning left interacting with oncoming manually driven vehicles (MV). The performance of the AV was compared to MV executing the same manoeuvre. The recorded video-based trajectories of the respective AV as well as MV were analysed regarding the influence of situational factors (e.g. position of the vehicle in the queue and gap acceptance) and kinematic factors (e.g. speed and acceleration) on traffic safety. The similarities and differences between this specific AV and MV were identified yielding insight for further developing algorithms for more efficient driving while maintaining the same traffic safety level. For instance, it appears that the AV shows a very conservative left turning behaviour leading to very safe PET distributions in comparison to left turning MV.
... In one experiment it was shown that when pedestrians were initially reacting to connected automated vehicles (CAVs), it was difficult for them to communicate with the CAVs. The interaction and communication between AVs and pedestrians affected crossing decisions [19]. Due to some miscommunications, initially with autonomous vehicles, it has caused accidents between the pedestrian and the vehicle. ...
Article
Level of emotional arousal of one's body changes in response to external stimuli in an environment. Given the risks involved while crossing streets, particularly at unsignalized mid-block crosswalks, one can expect a change in the stress level of pedestrians. In this study, we investigate the levels and changes in pedestrian stress, under different road crossing scenarios in immersive virtual reality. To measure stress level of pedestrians, we used Galvanic Skin Response (GSR) sensors. To collect the required data for the model, Virtual Immersive Reality Environment (VIRE) tool is used, which enables us to measure participant's stress levels in a controlled environment. Detailed experiments were conducted over a 5-month period, with 180 participants from four different places in Toronto to cover a heterogeneous population. Data collected are used to develop behavioural models, to observe the contribution of different variables on increasing pedestrian stress level. The initial modelling results suggested that the density of vehicles has a positive effect, meaning as the density of vehicles increases, so does the stress levels for pedestrians. The sociodemographic information has a relationship to individual’s stress levels. It was noted that younger pedestrians have lower amount of stress when crossing as compared to older pedestrians which have higher amounts of stress. Geometric variables has an impact on the stress level of pedestrians. The greater the number of lanes the greater the observed stress, which is due the crossing distance increasing, while the walking speed remaining the same.
Preprint
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Automated vehicles (AVs) may feature blinded (i.e., blacked-out) windows and external Human-Machine Interfaces (eHMIs), and the driver may be inattentive or absent, but how these features affect cyclists is unknown. In a crowdsourcing study, participants viewed images of approaching vehicles from a cyclist’s perspective and decided whether to brake. The images depicted different combinations of traditional versus automated vehicles, eHMI presence, vehicle approach direction, driver visibility/window-blinding, visual complexity of the surroundings, and distance to the cyclist (urgency). The results showed that the eHMI and urgency level had a strong impact on crossing decisions, whereas visual complexity had no significant influence. Blinded windows caused participants to brake for the traditional vehicle. A second crowdsourcing experiment aimed to clarify the findings of Experiment 1 by also requiring participants to detect the vehicle features. It was found that the eHMI ‘GO’ and blinded windows yielded high detection rates and that driver eye contact caused participants to continue pedalling. To conclude, blinded windows increase the probability that cyclists brake, and driver eye contact stimulates cyclists to continue cycling. Our findings, which were obtained with large international samples, may help elucidate how AVs (in which the driver may not be visible) affect cyclists’ behaviour.
Chapter
The full-scale deployment of autonomous driving demands successful interaction with pedestrians and other vulnerable road users, which requires an understanding of their dynamic behavior and intention. Current research achieves this by estimating pedestrian’s trajectory mainly based on the gait and movement information in the past as well as other relevant scene information. However, the autonomous vehicles still struggle with such interactions since the visual features alone may not supply subtle details required to attain a superior understanding. The decision-making ability of the system can improve by incorporating human knowledge to guide the vision-based algorithms. In this paper, we adopt a novel approach to retrieve human knowledge from the natural text descriptions about the pedestrian-vehicle encounters, which is crucial to anticipate the pedestrian intention and is difficult for computer vision (CV) algorithms to capture automatically. We applied natural language processing (NLP) techniques on the aggregated description from different annotators to generate a temporal knowledge graph, which can achieve the changes of intention and the corresponding reasoning processes in a better resolution. In future work, we plan to show that in combination with video processing algorithms, the knowledge graph has the potential to aid the decision-making process to be more accurate by passively integrating the reasoning ability of humans.
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Automated Mobility on Demand (AMoD) is a concept that has recently generated much discussion. In cases where large-scale adoption of an automated taxi service is anticipated, the service’s impacts may become relevant to key transport system metrics, and thus to transport planners and policy-makers as well. In light of this increasingly important question, this paper presents an agent-based transport simulation with (single passenger) AMoD. In contrast to earlier studies, all scenario data (including demand patterns, cost assumptions and customer behaviour) is obtained for one specific area, the city of Zurich, Switzerland. The simulation study fuses information from a detailed bottom-up cost analysis of mobility services in Switzerland, a specifically tailored Stated-Preferences survey about automated mobility services conducted in the canton of Zurich, and a detailed agent-based transport simulation for the city, based on MATSim. Methodologically, a comprehensive approach is presented that iteratively runs these components to derive states in which service cost, waiting times and demand are in equilibrium for a cost-covering AMoD operator with predefined fleet size. For Zurich, several cases are examined, with 4,000 AMoD vehicles leading to the maximum demand of around 150,000 requests per day that can be attracted by the system. Within these parameters, the simulation results show that customers are willing to accept average waiting times of around 4 min at a price of 0.75 CHF/km. Further cost-covering cases with lower demand are presented, where either smaller fleet sizes lead to higher waiting times, or larger fleet sizes lead to higher costs. While our simulations indicate that an AMoD system in Zurich can bring benefits to the users, they show that the system impact is largely negative. Caused by modal shifts, our simulations show an increase of driven distance of up to 100%. All examined fleet configurations of the unregulated, cost-covering, single-passenger, door-to-door AMoD service are found to be highly counter-productive on a path towards a more shared and active transport system. Accordingly, policy recommendations for regulation are discussed.
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The conventional form of traffic interaction undergoes a notable change with the integration of automated driving systems as a new road user, into the public roads. This may be more challenging during the transition phase, while manual-driven vehicles are still on the road, and the road infrastructure is not fully ready for merging such vehicles into the traffic patterns. Therefore, developing a robust interaction method is crucial to ensure the safety of those users interacting with automated driving systems and to ensure the efficiency of these systems on the road. For this purpose, the interaction of automated driving systems with pedestrians, as one of the most vulnerable road user groups, is investigated in this paper. Previous studies have shown the necessity for a comprehensive understanding of pedestrian behaviours and intentions, their responses to different stimuli on the road, the factors influencing their decisions during the interaction, and various external communication techniques among road users. As a result, a wide range of factors related to the communication environment, pedestrian characteristics, and existing communication methods have been found to be significant in the decision-making process of pedestrians.
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Intentionally blocking the path of fully automated vehicles is an important dimension of pedestrians’ receptivity towards these vehicles. The monetary value of this behaviour can be obtained by asking pedestrians about their perception of the “fine” for blocking the path of a fully automated vehicle. Econometric modelling of the reported fine can shed more light on factors influencing pedestrians’ receptivity towards fully automated vehicles. However, development of such an econometric model is not straightforward due to the unique characteristics of the dependent variable: it has two fundamentally different states; it is right-truncated; and it may be fat-tailed. Despite fairly extensive methodological advancements in econometric modelling of pedestrian behaviour, there is no model that can adequately explain these characteristics. While a beta distribution in a hurdle setting has the potential to address the above complexities, its applicability in dealing with limited dependent variables in transport applications has remained, by and large, unexplored. This study aims to fill this gap by developing a new beta hurdle regression model that systematically considers the dual-state of a right-truncated dependent variable representing the fine associated with intentionally blocking a fully automated vehicle. The hypothesized model is empirically tested using data obtained from a survey administered in Queensland, Australia, and the results are compared with truncated lognormal, and truncated lognormal hurdle regression models. Results indicate that the hurdle models are superior to the non-hurdle model. The beta variant of the hurdle model provides a better statistical fit for the data that are near their right limit. In addition, parametrizing the variance of the beta distribution captures the additional heterogeneity in the data. Age, gender, education level, violations, attitudes, behaviours that appease social interactions, and perceived ease or difficulty of interacting with fully automated vehicles influence the likelihood and/or the propensity of the fine and thus are associated with the perceived monetary value of intentionally blocking the path of a fully automated vehicle.
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Automated vehicles (AVs) aim to dramatically improve traffic safety by reducing or eliminating human error, which remains the leading cause of road crashes. However, commonly accepted standards for the ‘safe driving behaviour of machines’ are pending and urgently needed. Unless a common understanding of safety as a design value is achieved, different manufacturers’ driving styles may emerge, resulting in inconsistent, unpredictable and potentially unsafe ‘behaviour’ of AVs in certain situations. This paper aims to explore the main gaps and challenges towards establishing shared safety standards for the ‘behaviour’ of AVs, and contribute to their responsible traffic integration, by reviewing the state-of-the-art on AV safety in the core relevant disciplines: ethics of technology, safety science (engineering & human factors), and standardisation. The ethical and safety aspects investigated include the users’ perception of AV safety, the ethical trade-offs in critical decision-making contexts, the pertinence of data-driven approaches for AVs to mimic human behaviour, and the responsibilities of various actors. Moreover, the paper reviews the current safety patterns, metrics (surrogate measures of safety – SMoS) and their thresholds introduced in existing research for three use cases: mixed traffic of AV and conventional vehicles, AV interaction with pedestrians and cyclists, and transition of control from machine to human driver. The results reveal several knowledge gaps within each discipline and highlights the lack of common understanding of safety across disciplines. On the basis of the results, the paper proposes a framework for further research on AV safety, identifying concrete opportunities for interdisciplinary research, with common goals and methodologies, and explicitly indicating the path for transfer of knowledge between sectors.
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Intelligent Transport System (ITS) involves a number of Information and Communication Technology (ICT) interventions for efficient road safety management. Over the last few decades, there has been a significant increase in the safety of Vulnerable Road Users (VRU's). This study aims to identify the most frequent, regularly occurring causes of road accidents and outline actions that can be used as a baseline for improving road traffic safety. The main contributions in this paper include the following: a graphical investigation for finding out the overall rate of road accidents (injuries and deaths) in India. Based on graphical analysis, major loopholes responsible for road accidents in India are summed up. This paper discusses various issues faced by already available pedestrian and vehicular safety techniques. After analysis of the available techniques and their related case studies in a systematic way, it is observed that they are not able to overcome the loopholes (Collision Occurrence, Traffic Congestion, Pedestrian Crossing alert, Red Light violation, and Non-Propagation of Distress Signal) present in the current “Road Transport Management (RTM) System” of India. Finally, after deep analysis of already available techniques, a futuristic approach “Mesh-Networking based Vehicle Ad-Hoc System (MN-VAS) using hybrid of Neuro-Fuzzy and Genetic Algorithm based Load Balancing on Node” is proposed for the safety of VRU's. This hybrid approach has been discussed with domain experts for practical input, and we concluded that this might be a good option for better RTM System in India.
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While virtual reality (VR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of VR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to VR technology, at present, the pool of findings is heterogeneous and non-transparent. We investigated N = 176 scientific papers of relevant journals and conferences with the goal to analyze the status quo of existing VR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of VR technology which is applicable at specific level of vehicle automation and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on designing automotive experiences in VR, safety aspects, and vulnerable road users. Trust, simulator and motion sickness, and external human-machine interfaces (eHMIs) also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Finally, we discuss a set of open challenges, and give recommendation for future research in automated driving at the VR side of the reality-virtuality continuum.
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With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3 to 3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.
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Background: Pedestrians are one of the most vulnerable groups of road users that potentially are at risk for road traffic injuries and deaths. The present paper reports an application of the Prototype Willingness Model (PWM) to the prediction of road-crossing behaviors among students from Kermanshah University of Medical Sciences (KUMS) in the west of Iran. Methods: This cross-sectional study was carried out among a sample of 315 medical students who were randomly selected from seven faculties of KUMS in 2017 according to their size, and who filled out a self-administered questionnaire containing a scenario depicting a potentially hazardous road-crossing behavior, followed by items measuring the PWM constructs. Data were analyzed by SPSS version 16 at 95% significant level. Results: The mean score of safe road-crossing behaviors was 9.57 [95% CI: 9.10, 10.05], ranging from 0 to 16. Attitude, subjective norms, and prototype accounted for 15% and 9% of the variation of willingness and intention, respectively. Willingness was a stronger predictor of the safe road-crossing behaviors (P less than 0.001). The road crossing behavior of female student pedestrian was safer than that of their male counterparts (P less than 0.035). Conclusions: The results have a number of implications. In particular, PWM-based interventions should focus on
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As the desire for deploying automated (“driverless”) vehicles increases, there is a need to understand how they might communicate with other road users in a mixed traffic, urban, setting. In the absence of an active and responsible human controller in the driving seat, who might currently communicate with other road users in uncertain/conflicting situations, in the future, understanding a driverless car’s behaviour and intentions will need to be relayed via easily comprehensible, intuitive and universally intelligible means, perhaps presented externally via new vehicle interfaces. This paper reports on the results of a questionnaire-based study, delivered to 664 participants, recruited during live demonstrations of an Automated Road Transport Systems (ARTS; SAE Level 4), in three European cities. The questionnaire sought the views of pedestrians and cyclists, focussing on whether respondents felt safe interacting with ARTS in shared space, and also what externally presented travel behaviour information from the ARTS was important to them. Results showed that most pedestrians felt safer when the ARTS were travelling in designated lanes, rather than in shared space, and the majority believed they had priority over the ARTS, in the absence of such infrastructure. Regardless of lane demarcations, all respondents highlighted the importance of receiving some communication information about the behaviour of the ARTS, with acknowledgement of their detection by the vehicle being the most important message. There were no clear patterns across the respondents, regarding preference of modality for these external messages, with cultural and infrastructural differences thought to govern responses. Generally, however, conventional signals (lights and beeps) were preferred to text-based messages and spoken words. The results suggest that until these driverless vehicles are able to provide universally comprehensible externally presented information or messages during interaction with other road users, they are likely to contribute to confusing and conflicting interactions between these actors, especially in a shared space setting, which may, therefore, reduce efficient traffic flow.
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The number of pedestrian casualties in crashes with motorised vehicles is still alarming. Misunderstandings about the other road users’ intentions are certainly one contributory factor. Especially given recent developments in vehicle automation, informing about “vehicle behaviour” and “vehicle intentions” in the absence of any direct interaction between the driver and the outside world is becoming increasingly relevant. A frontal brake light which communicates that a vehicle is decelerating could be a simple approach to support pedestrians and other road users in the interaction with (potentially automated) motorised vehicles. To assess the effect of a frontal brake light on the identification of vehicle deceleration, we conducted a video based lab experiment. The brake light facilitated the identification of decelerations considerably. At the same time, the fact that only half of the decelerations were accompanied by the brake light resulted in increased identification times for decelerations in which the frontal brake light was absent compared to a control condition in which none of the decelerations was indicated by such a light. This finding points towards an increasingly conservative approach in the participants’ assessment of deceleration, which could be interpreted as an indicator for a potential safety effect of the frontal brake light.
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This paper presents a novel simulation model that shows the dynamic and complex nature of the innovation system of vehicle automation in a quantitative way. The model simulates the innovation diffusion of automated vehicles (AVs) on the long-term. It looks at the system of AVs from a functional perspective and therefore categorizes this technology into six different levels. Each level is represented by its own fleet size, its own technology maturity and its own average purchase price and utility. These components form the core of the model. The feedback loops between the components form a dynamic behavior that influences the diffusion of AVs. The model was applied to the Netherlands both for a base and an optimistic scenario (strong political support and technology development) named "AV in-bloom". In these experiments, we found that the system is highly uncertain with market penetration varying greatly with the scenarios and policies adopted. Having an 'AV in bloom' ecosystem for AVs is connected with a great acceleration of the market take-up of high levels of automation. As a policy instrument, a focus on more knowledge transfer and the creation of an external fund (e.g. private investment funds or European research funds) has shown to be most effective to realize a positive innovation diffusion for AVs. Providing subsidies may be less effective as these give a short-term impulse to a higher market penetration, but will not be able to create a higher market surplus for vehicle automation.
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When pedestrians encounter vehicles, they typically stop and wait for a signal from the driver to either cross or wait. What happens when the car is autonomous and there isn't a human driver to signal them? This paper seeks to address this issue with an intent communication system (ICS) that acts in place of a human driver. This intent system has been developed to take into account the psychology behind what pedestrians are familiar with and what they expect from machines. The system integrates those expectations into the design of physical systems and mathematical algorithms. The goal of the system is to ensure that communication is simple, yet effective without leaving pedestrians with a sense of distrust in autonomous vehicles. To validate the ICS, two types of experiments have been run: field tests with an autonomous vehicle to determine how humans actually interact with the ICS and simulations to account for multiple potential behaviors.The results from both experiments show that humans react positively and more predictably when the intent of the vehicle is communicated compared to when the intent of the vehicle is unknown. In particular, the results from the simulation specifically showed a 142 percent difference between the pedestrian's trust in the vehicle's actions when the ICS is enabled and the pedestrian has prior knowledge of the vehicle than when the ICS is not enabled and the pedestrian having no prior knowledge of the vehicle.
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Researchers, technology reviewers, and governmental agencies have expressed concern that automation may necessitate the introduction of added displays to indicate vehicle intent in vehicle-to-pedestrian interactions. An automated online methodology for obtaining communication intent perceptions for 30 external vehicle-to-pedestrian display concepts was implemented and tested using Amazon Mechanic Turk. Data from 200 qualified participants was quickly obtained and processed. In addition to producing a useful early-stage evaluation of these specific design concepts, the test demonstrated that the methodology is scalable so that a large number of design elements or minor variations can be assessed through a series of runs even on much larger samples in a matter of hours. Using this approach, designers should be able to refine concepts both more quickly and in more depth than available development resources typically allow. Some concerns and questions about common assumptions related to the implementation of vehicle-to-pedestrian displays are posed.
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The last mile in a public transport trip is known to bring a large disutility for passengers, because the conventional transport modes for this stage of the trip can, in many cases, be rather slow, inflexible and not being able to provide a seamless experience to passengers. Fully automated vehicles (AVs), that is, those which do not need a driver, could act as a first mile/last mile connection to mass public transport modes. In this paper, we study a system that we call Automated Last-Mile Transport (ALMT), which consists of a fleet of small, fully automated, electric vehicles to improve the last mile performance of a trip done in a train. An agent-based simulation model was proposed for the ALMT whereby a dispatching algorithm distributes travel requests amongst the available vehicles using a FIFO sequence and selects a vehicle based on a set of specified control conditions (e.g. travel time to reach a requesting passenger). The model was applied to the case-study of the connection between the train station Delft Zuid and the Technological Innovation Campus (Delft, The Netherlands) in order to test the methodology and understand the performance of the system in function of several operational parameters and demand scenarios. The most important conclusion from the baseline scenario was that the ALMT system was only able to compete with the walking mode and that additional measures were needed to increase the performance of the ALMT system in order to be competitive with cycling. Relocating empty vehicles or allowing pre-booking of vehicles led to a significant reduction in average waiting time, whilst allowing passengers to drive at a higher speed led to a large reduction in average travel time, whilst simultaneously reducing system capacity as energy use is increased.
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Despite enthusiastic speculation about the potential benefits of self-driving cars, to date little is known about the factors that will affect drivers’ acceptance or rejection of this emerging technology. Gaining acceptance from end users will be critical to the widespread deployment of self-driving vehicles. Long-term acceptance may be harmed if initial acceptance is built upon unrealistic expectations developed before people interact with these systems. A brief (24-item) measurement scale was created to assess acceptance of self-driving cars. Before completing the scale, participants were randomly assigned to read short vignettes that featured either a realistic or an idealistic description of a friend’s experiences during the first six months of owning a self-driving car. A small but significant effect showed that reading an idealized portrayal in the vignette resulted in higher acceptance of self-driving cars. Potential factors affecting user acceptance of self-driving cars are discussed. Establishing realistic expectations about the performance of automation before users interact with self-driving cars may be important for long-term acceptance.
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Automated driving technology is emerging. Yet, little is known in the literature about when automated vehicles will reach the market, how penetration rates will evolve and to what extent this new transport technology will affect transport demand and planning. This study uses scenario analysis to identify plausible future development paths of automated vehicles in the Netherlands and to estimate potential implications for traffic, travel behaviour and transport planning on a time horizon up to 2030 and 2050. The scenario analysis was performed through a series of three workshops engaging a group of diverse experts. Sixteen key factors and five driving forces behind them were identified as critical in determining future development of automated vehicles in the Netherlands. Four scenarios were constructed assuming combinations of high or low technological development and restrictive or supportive policies for automated vehicles (AV …in standby, AV …in bloom, AV …in demand, AV …in doubt). According to the scenarios, fully automated vehicles are expected to be commercially available between 2025 and 2045, and to penetrate the market rapidly after their introduction. Penetration rates are expected to vary among different scenarios between 1% and 11% (mainly conditionally automated vehicles) in 2030 and between 7% and 61% (mainly fully automated vehicles) in 2050. Complexity of the urban environment and unexpected incidents may influence development path of automated vehicles. Certain implications on mobility are expected in all scenarios, although there is great variation in the impacts among the scenarios. Measures to curb growth of travel and subsequent externalities are expected in three out of the four scenarios.
Conference Paper
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Autonomous cars seem on the verge to reality, with vehicle manufacturers presenting their first prototypes and the topic of self-driving vehicles being discussed in mass media. Until now, in individual traffic humans covered distances from A to B using a personal car or a motorcycle, riding a bike or walking by foot (from strongest to weakest modality). All these modalities coexist in parallel in typical traffic situations, and it should be clear that different situations require clarification and communication between the different road participants, e.g., to negotiate who has right of way and who has to wait. Many drivers spend a considerable time each day in their car – for commuting, shopping, and traveling. In order to save time for the driver it is expected that manual driving will be eliminated in the near future and replaced by automated systems. One of the problems not brought up by autonomous vehicle manufacturers so far is when the " strongest " road user (vehicle or truck) is no longer human-driven, as then the chance for vulnerable road users (VRUs) to communicate, interact and negotiate could be evicted too. In this work, based on the showcase of Mercedes Benz's F015 at CES this year, we want to show that it is important to substitute the means of pedestrian-vehicle communication by autonomous cars to understand the signs and gestures of pedestrians and also communicating actively (e.g., using visual feedback on windscreen, bonnet or headlights) towards them. To get a deeper knowledge of this scenario, we will setup and conduct a user study, placing subjects into situations with a presumably autonomous car and comparing the actions and reactions with and without the car explicitly interacting with the subject. Our expectation is to detect a difference in the behavior of the pedestrians that will reveal a different level of trust and confidence towards autonomous cars.
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Autonomous vehicles (AVs) represent a potentially disruptive yet beneficial change to our transportation system. This new technology has the potential to impact vehicle safety, congestion, and travel behavior. All told, major social AV impacts in the form of crash savings, travel time reduction, fuel efficiency and parking benefits are estimated to approach $2000 to per year per AV, and may eventually approach nearly $4000 when comprehensive crash costs are accounted for. Yet barriers to implementation and mass-market penetration remain. Initial costs will likely be unaffordable. Licensing and testing standards in the U.S. are being developed at the state level, rather than nationally, which may lead to inconsistencies across states. Liability details remain undefined, security concerns linger, and without new privacy standards, a default lack of privacy for personal travel may become the norm. The impacts and interactions with other components of the transportation system, as well as implementation details, remain uncertain. To address these concerns, the federal government should expand research in these areas and create a nationally recognized licensing framework for AVs, determining appropriate standards for liability, security, and data privacy.
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This paper presents a new concept of urban shared-taxi services. The proposed system has a new organisational design and pricing scheme that aims to use the capacity in traditional taxi services in a more efficient way. In this system, a taxi acting in ‘sharing’ mode offers lower prices to its clients, in exchange for them to accept sharing the vehicle with other persons who have compatible trips (time and space). The paper proposes and tests an agent-based simulation model in which a set of rules for space and time matching between a request of a client and the candidate shared taxis is identified. It considers that the client is only willing to accept a maximum deviation from his or her direct route and establishes an objective function for selecting the best candidate taxi. The function considers the minimum travel time combination of pickup and drop-off of all the pool of clients sharing each taxi while allowing to establish a policy of bonuses to competing taxis with certain number of occupants. An experiment for the city of Lisbon is presented with the objectives of testing the proposed simulation conceptual model and showing the potential of sharing taxis for improving mobility management in urban areas. Results show that the proposed system may lead to significant fare and travel time savings to passengers, while not jeopardising that much the taxi revenues. Copyright © 2014 John Wiley & Sons, Ltd.
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A two-pronged study was conducted to investigate (a) pedestrians' road-crossing behaviour and (b) perceptions of the walking environment, both before and after the installation of a marked crosswalk (zebra crossing) at a single case-study location in Edinburgh, UK. The observational and questionnaire surveys indicated that: (a) pedestrians were significantly more likely to use the location to cross the road, waited significantly less time to cross, and walked significantly more slowly after the zebra had been installed compared with before: and (b) people felt safer, less vulnerable to traffic and more confident when crossing the road after the zebra had been installed. The results indicate that installing a marked crosswalk such as a zebra crossing can significantly enhance the road-crossing experience of pedestrians and therefore improve the walking journey more broadly.
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This paper reports a study applying the Theory of Planned Behaviour (TPB) to the prediction of pedestrians' road crossing intentions. Respondents (N = 210) completed questionnaires which included scenarios of three potentially dangerous road crossing behaviours, followed by measures of attitude, subjective norm, perceived behavioural control, self-identity and intention. The results indicated that the social psychological variables under consideration were able to explain between 39 and 52% of the variance in intentions to cross the road in the manner depicted in the scenarios. The perceived behavioural control component of the TPB emerged as the strongest predictor of pedestrians' intentions, suggesting that perceptions of control have an important role to play in road safety behaviour. The results are discussed in relation to the predictive utility of the TPB in this area and possible interventions to encourage safe road crossing behaviour.
Article
Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the organizational, sociological, interpersonal, psychological, and neurological perspectives. It considers how the context, automation characteristics, and cognitive processes affect the appropriateness of trust. The context in which the automation is used influences automation performance and provides a goal-oriented perspective to assess automation characteristics along a dimension of attributional abstraction. These characteristics can influence trust through analytic, analogical, and affective processes. The challenges of extrapolating the concept of trust in people to trust in automation are discussed. A conceptual model integrates research regarding trust in automation and describes the dynamics of trust, the role of context, and the influence of display characteristics. Actual or potential applications of this research include improved designs of systems that require people to manage imperfect automation. Copyright © 2004, Human Factors and Ergonomics Society. All rights reserved.
Conference Paper
Current vehicle-pedestrian interactions involve the vehicle communicating cues through its physical movement and through nonverbal cues from the driver. Our work studies vehicle-pedestrian interactions at a crosswalk in the presence of autonomous vehicles (without a driver) facilitated by the deployment of interfaces intended to replace missing driver cues. We created four prototype interfaces based on different modalities (such as visual, auditory, and physical) and locations (on the vehicle, on street infrastructure, on the pedestrian, or on a combination of the vehicle, street infrastructure, and the pedestrian). Our findings from two user studies indicate that interfaces which communicate awareness and intent can help pedestrians attempting to cross. We also find that interfaces are not limited to existing only on the vehicle.
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Research in the field of autonomous vehicle technology focuses on its enhanced safety and convenience for vehicle occupants. The present paper seeks to establish a line of inquiry that addresses the implications of autonomous vehicle technology for nonmotorized road users, in the present case, bicyclists. Studies show that motorized traffic volume and speed affect nonmotorized agents' behavior and facility preference, but the degree to which this will apply to a driverless environment requires further study. We developed a stated-preference survey that had respondents select their preferred facility in a variety of hypothetical scenarios with and without the presence of driverless vehicles and on street types of varying motorized traffic volumes and speeds. A Random Parameters Logit Model is estimated to analyze the links between facility preferences, sociodemographics, street types, and the existence of driverless vehicles. The model results suggest that increases in motorized traffic volumes and speeds correlate with a greater preference for separated facilities. The presence of driverless vehicles amplifies this preference. Controlling for other factors, under driverless vehicle conditions, the odds of selecting protected facilities, such as buffered bicycle lanes and cycle tracks, were more than double the odds found under current conditions. We conclude with recommendations for infrastructure and policy and suggestions for future research in this nascent field of study.
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Fully automated self-driving cars, with expected benefits including improved road safety, are closer to becoming a reality. Thus, attention has turned to gauging public perceptions of these autonomous vehicles. To date, surveys have focused on the public as potential passengers of autonomous cars, overlooking other road users who would interact with them. Comparisons with perceptions of other existing vehicles are also lacking. This study surveyed almost 1000 participants on their perceptions, particularly with regards to safety and acceptance of autonomous vehicles. Overall, results revealed that autonomous cars were perceived as a “somewhat low risk“ form of transport and, while concerns existed, there was little opposition to the prospect of their use on public roads. However, compared to human-operated cars, autonomous cars were perceived differently depending on the road user perspective: more risky when a passenger yet less risky when a pedestrian. Autonomous cars were also perceived as more risky than existing autonomous trains. Gender, age and risk-taking had varied relationships with the perceived risk of different vehicle types and general attitudes towards autonomous cars. For instance, males and younger adults displayed greater acceptance. Whilst their adoption of this autonomous technology would seem societally beneficial – due to these groups’ greater propensity for taking road user risks, behaviours linked with poorer road safety – other results suggested it might be premature to draw conclusions on risk-taking and user acceptance. Future studies should therefore continue to investigate people’s perceptions from multiple perspectives, taking into account various road user viewpoints and individual characteristics.
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Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with ‘‘self-driving” sign on the roof, inattentive driver in a vehicle with ‘‘self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures.
Article
This study analyzes pedestrian receptivity toward fully autonomous vehicles (FAVs) by developing and validating a pedestrian receptivity questionnaire for FAVs (PRQF). The questionnaire included sixteen survey items based on attitude, social norms, trust, compatibility, and system effectiveness. 482 Participants from the United States (273 males and 209 females, age range: 18–71 years) responded to an online survey. A principal component analysis determined three subscales describing pedestrians’ receptivity toward FAVs: safety, interaction, and compatibility. This factor structure was verified by a confirmatory factor analysis and reliability of each subscale was confirmed (0.7 < Cronbach’s alpha < 0.9). Regression analyses investigated associations with scenario-based responses to the three PRQF subscale scores. Pedestrians’ intention to cross the road in front of FAVs was significantly predicted by both safety and interaction scores, but not by the compatibility score. Accepting FAVs in the existing traffic system was predicted by all three subscale scores. Demographic influence on the receptivity revealed that males and younger respondents were more receptive toward FAVs. Similarly, those from urban areas and people with higher personal innovativeness showed higher receptivity. Finally, a significant effect of pedestrian behavior (as measured by the pedestrian behavior questionnaire) on receptivity is explored. People who show positive behavior believed that the addition of FAVs will improve overall traffic safety. Those who show higher violation, lapse and aggression scores, were found to feel more confident about crossing the road in front of a FAV. This questionnaire can be a potential research tool for designing and improving FAVs for road-users outside the vehicles.
Conference Paper
With autonomous cars being released into the wild, the question is how they integrate with conventional participants in traffic, such as pedestrians. For accident-free traffic, it is necessary for autonomous cars to interact, collaborate, and negotiate with other agents. Knowledge from social robots provides a valuable source for new interaction paradigms for autonomous cars. We raise the question of how autonomous cars communicate their intentions to pedestrians and negotiate in conflict situations. We discuss the scenario and present three possible communication strategies that are informed from human-robot interaction (HRI).
Article
Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.
Conference Paper
Highly automated test vehicles are rare today, and (independent) researchers have often limited access to them. Also, developing fully functioning system prototypes is time and effort consuming. In this paper, we present three adaptions of the Wizard of Oz technique as a means of gathering data about interactions with highly automated vehicles in early development phases. Two of them address interactions between drivers and highly automated vehicles, while the third one is adapted to address interactions between pedestrians and highly automated vehicles. The focus is on the experimental methodology adaptations and our lessons learned.
Article
The objective of this research is the development of pedestrian crossing choice models on the basis of road, traffic and human factors. For that purpose, a field survey was carried out, in which a panel of 75 pedestrians were asked to take 8 short walking trips (each one corresponding to a different walking and crossing scenario) in the Athens city centre in Greece, allowing to record their crossing behaviour in different road and traffic conditions. The same individuals were asked to fill in a questionnaire on their travel motivations, their mobility characteristics, their risk perceptions and preferences with respect to walking and road crossing, their opinion on drivers, etc. The walking and crossing scenarios’ data were used to develop mixed sequential logit models of pedestrian behaviour on the basis of road and traffic characteristics. The modelling results showed that pedestrian crossing choices are significantly affected by road type, traffic flow and traffic control. The questionnaire data were used to estimate human factors (components) of pedestrian crossing behaviour by means of principal component analysis. The results showed that three components of pedestrian crossing behaviour emerge, namely a “risk-taking and optimisation” component reflecting the tendency to cross at mid-block in order to save time, etc., a “conservative” component, concerning individuals with increased perceived risk of mid-block crossing, who also appear to be frequent public transport users, and a “pedestrian for pleasure” component, bringing together frequent pedestrians, walking for health or pleasure, etc. The introduction of these components as explanatory variables into the choice models resulted in improvement of the modelling results, indicating that human factors have additional explanatory power over road and traffic factors of pedestrian behaviour. Therefore, the development of integrated choice and latent variables models appears to be an appropriate field for further research.
Article
In the disaster management system, using informationalized technology to deal and prevent with the disaster has already became an important way. When disasters occurred, the refuge roads play an important role for ensuring the safety of the crowd and reducing the damagement. At present, the refuge road in urban business central district in disaster emergency management is still lack of sufficient research. Especially the study on the disaster evacuation management simulation research. The study concentrate on the simulating the crowd evacuation when the earthquake happened in the commercial pedestrian street in ChongQing Sha Pingba district. With the consideration of characteristics of the space and the crowd. And then select the main factor which will affect the evacuation efficiency and qualified to establish the model. The simulation provides the city visualization of three-dimensional space crowd emergency evacuation process and under such circumstances the different escape time of different aged group. Responsing the shortage of when the commercial pedestrian street facing the emergency evacuation in directly. In order to let the disaster management department help the refugee population develop more targeted, effective and safe way of emergency evacuation. And by the perspective of emergency management, make control and improve of the city planning of commercial pedestrian street at the same time. Further more, the simulation can be docking to the next higher level which will promote the improvement of the urban disaster management mechanism.
Conference Paper
The study of pedestrian simulation is one of the remarkable topics in the current traffic and simulation field. The meanings and contents of the study were stated in the beginning. A brief history of the study was discussed. In the next, the mechanism and applicable conditions of five typical microscopic pedestrian simulation models, including benefit cost cellular model, cellular automata model, magnetic force model, social force model and queuing network model, were analyzed besides a comparison. Furthermore, five pieces of typical pedestrian simulation software were proposed. Finally, the problems and the future tendencies of pedestrian simulation were discussed.
Article
The present study examines the road behaviour of individual pedestrians at an intersection with a traffic signal compared to groups of pedestrians at the same intersection.In total, 1392 pedestrians were unobtrusively observed in an urban setting at a pedestrian street crossing of undivided streets; 842 were female (60.5%) and 550 were male (39.5%). The observations took place between 7:30 and 8:30 in the morning. Chi-square test revealed the males crossed on red more frequently than females. Logistic regression predicting red-light crossing for pedestrians arriving during a red-light phase indicated that, apart from gender, the tendency to cross on red was greater when there were fewer people waiting at the curb, either when a pedestrian arrived, or joining after arrival. The discussion refers to the theoretical explanations concerning the theory of ‘social control’ and to some practical implications of the results, such as using the positive value of social control in media campaigns and adjusting the red light duration in order to encourage people to obey the traffic light.
The use of advanced communication media may enhance the social networks of older adults. Although many older adults are open to new technology, there are still barriers that keep them from learning and using media such as e-mail and the Intemet. Besides lacking skills, the lack of perceived advantages, or benefits, may also explain their reluctance. The goal of the present study was to investigate perceived context-related benefits of communication methods by older adults. Forty-eight independently living older adults in the age range of 65-80 years, 24 e-mail users and 24 non-users, participated in a focus group discussion of different communication scenarios. A systematic analysis of their comments and statements showed the relevance of perceived context-related benefit as a motivational factor for using or not using a medium. An implication of these results may be that training the skills to handle a new technology should also involve providing information about its specific benefits, from the user's perspective.
Article
Aim of the presented research is the development of a cognitive driver assistance system, which can capture the traffic situation, analyse it, and warn the driver in case a pedestrian is a potential hazard. Hence parameters have to be identified by which the intention of the pedestrian can be unambiguously predicted. Two approaches to the topic are addressed. First, the pedestrian’s perspective was taken. The question was how crossing decisions were influenced by the parameters distance and velocity of the car. Following a signal, participants had to choose to cross the road in front of or behind the car. The data analysis showed that pedestrians relied on the distance of the car rather than the time to collision for their decision. In the second experiment the observer’s perspective raised the question what parameters humans use to predict pedestrians’ intentions. Videos of natural traffic scenes were presented. Participants had to make statements about whether the shown pedestrian would cross the street during the next moment. In a baseline and four experimental conditions, certain information was masked in the videos. Just the condition in which only the trajectory information of the pedestrian was available produced a higher error rate.
Article
This study aimed to examine responses of sensation seekers concerning their tendency to take risks in driving in mortality salience. Ss completed the Zuckerman's Sensation Seeking scale (SSS). Two weeks later Ss were divided into two groups; the experimental group, which was exposed to a terrifying video film dealing with consequences of risky driving and a control group with a nature video film. After watching the films, each participant was asked to complete a risk-taking inventory (RT), which referred to the extent of risk s/he would take while driving. High sensation seekers reported more risk taking in driving than sensation avoiders. Furthermore, a significant interaction was found between Mortality Salience and Sensation Seeking regarding risky driving, especially speeding. The implications of these findings on the well-established educational approaches based on terror are presented in the discussion.
Conference Paper
This paper discusses simulation of the vehicle schedule with time windows. The simulation model is developed in AnyLogic simulation environment. A short overview of AnyLogic structure and scope is provided. A detailed description of a conceptual model with focus on input and output data is given. The structure of the simulation model and operation of its main blocks are described. Each vehicle is modelled as a separate object used to construct the overall schedule for all vehicles. The simulation model is used as a decision support tool for an analyst, which allows estimating efficiency of vehicle schedules with time windows generated by a standard software or/and modified by a planner.
Article
Road traffic crashes (RTCs) are responsible for a substantial fraction of morbidity and mortality and are responsible for more years of life lost than most of human diseases. In this review, we have tried to delineate behavioral factors that collectively represent the principal cause of three out of five RTCs and contribute to the causation of most of the remaining. Although sharp distinctions are not always possible, a classification of behavioral factors is both necessary and feasible. Thus, behavioral factors can be distinguished as (i) those that reduce capability on a long-term basis (inexperience, aging, disease and disability, alcoholism, drug abuse), (ii) those that reduce capability on a short-term basis (drowsiness, fatigue, acute alcohol intoxication, short term drug effects, binge eating, acute psychological stress, temporary distraction), (iii) those that promote risk taking behavior with long-term impact (overestimation of capabilities, macho attitude, habitual speeding, habitual disregard of traffic regulations, indecent driving behavior, non-use of seat belt or helmet, inappropriate sitting while driving, accident proneness) and (iv) those that promote risk taking behavior with short-term impact (moderate ethanol intake, psychotropic drugs, motor vehicle crime, suicidal behavior, compulsive acts). The classification aims to assist in the conceptualization of the problem that may also contribute to behavior modification-based efforts.
Article
The over-representation of older pedestrians in serious injury and fatal crashes compared to younger adults may be due, in part, to age-related diminished ability to select gaps in oncoming traffic for safe road-crossing. Two experiments are described that examine age differences in gap selection decisions in a simulated road-crossing environment. Three groups of participants were tested, younger (30-45 years), young-old (60-69 years) and old-old (>75 years). The results showed that, for all age groups, gap selection was primarily based on vehicle distance and less so on time-of-arrival. Despite the apparent ability to process the distance and speed of oncoming traffic when given enough time to do so, many of the old-old adults appeared to select insufficiently large gaps. These results are discussed in terms of age-related physical, perceptual and cognitive limitations and the ability to compensate for these limitations. Practical implications for road safety countermeasures are also highlighted, particularly the provision of safe road environments and development of behavioural and training packages.
Article
The theory of planned behaviour (TPB) has been used successfully in the past to account for pedestrians' intentions to cross the road in risky situations. However, accident statistics show age and gender differences in the likelihood of adult pedestrian accidents. This study extends earlier work by examining the relative importance of the model components as predictors of intention to cross for four different adult age groups, men, women, drivers and nondrivers. The groups did not differ in the extent to which they differentiated between two situations of varying perceived risk. The model fit was good, but accounted for less of the variance in intention for the youngest group (17-24) than for other age groups. Differences between the age groups in intention to cross seemed to be due to differences in perceived value of crossing rather than differences in perceived risk. Women were less likely to intend to cross than men and perceived more risk, and there were important age, gender and driver status differences in the importance of the TPB variables as predictors of intention. A key implication of these findings is that road safety interventions need to be designed differently for different groups.
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