Dynamic traffic management on a familiar road: Failing to detect changes in variable speed limits

Variable speed limits (VSL) are used more commonly around the globe lately. Although on a macroscopic level positive effects of VSLs have been reported, the caveat is that the impact of VSLs is very sensitive to the level of driver compliance. Thus far it is unknown whether all individual drivers are actually able to notice when a speed limit changes into another speed limit; a prerequisite for purposeful speed limit compliance in the first place. To simulate regular driving conditions, twenty-four participants were familiarised with a particular route by driving the same route in a driving simulator nineteen times on five separate days. Part of the route consisted of a motorway where VSL signs were regularly displayed above every driving lane. At drive nineteen, speed limits changed from 80 km/h to 100 km/h on four out of eight consecutive signs. After passing all signs, one expects 6.25% of the participants still to be unaware that the speed limit had increased (based on chance), while the results showed most participants had failed to notice the speed limit change (58.3%). Instead, they saw what they expected to see: a speed limit of 80 km/h. If the speed change had been vice versa, in other words from 100 km/h to 80 km/h, this would immediately result in speed offences, though not deliberately at all.     

Recovery from post‐traumatic stress disorder in children following road traffic accidents: the role of talking and feeling understood

Forty children were assessed 6 weeks and 8 months after involvement in a road traffic accident (RTA). Ten of the 21 children suffering post‐traumatic stress disorder (PTSD) at 6 weeks continued to fulfil diagnostic criteria at 8 months. There was no evidence of delayed onset of PTSD in children who had not developed this condition at 6 weeks. Talking about the accident and feeling understood were associated with recovery. Providing children with opportunities to talk about their accident may be helpful in preventing or reducing psychological distress. Copyright © 2001 John Wiley & Sons, Ltd.     

Towards statistical multicriteria decision modelling: a first approach

Many real life situations result from decisions taken by a very large number of decision makers. Among them, we may cite road traffic congestion, crowding during shopping, equity market behaviour, distribution of holiday destinations, etc. Furthermore, these decisions often depend on the optimisation of several conflicting criteria. In this paper, we introduce a new multicriteria tool based on Markov chains to model and manage these macroscopic phenomena. Finally, the road traffic congestion problem will be considered to illustrate the applicability of our approach. Copyright © 2003 John Wiley & Sons, Ltd.     

Convergent,discriminant, and predictive validity of aggressive driving inventories: they drive as they live

The authors hypothesized that aggressive driving is indicative of a more general trait that leads individuals to behave aggressively in different situations. This hypothesis was tested by having 85 male and 237 female undergraduates complete the Aggression Questionnaire, three measures of aggressive driving (the Driver Anger Scale, Driver Vengeance Questionnaire, and Driving Behavior Inventory), and questions about violating traffic laws. High correlations among the aggressive driving inventories provided evidence for the convergent validity, but a confirmatory factor analyses raised questions about the discriminant validity of aggressive driving inventories. The inventories did not attain significance in hierarchical analyses of traffic violations that controlled for gender, driving experience, and measures of trait aggression. It is concluded that there is little unique about aggressive driving and that individuals drive as they live. Aggr. Behav. 32:89–98, 2006. © 2006 Wiley‐Liss, Inc.     

Tracking the identity of moving words: Stimulus complexity and familiarity affects tracking accuracy

In two experiments, participants tracked the identity and location of moving words. The task bears resemblance to one performed by air traffic controllers who track multiple moving aircraft, where they are identified with relatively complex alphanumeric call signs. In Experiment 1, stimulus familiarity was manipulated by comparing the tracking of familiar words and pseudowords. In Experiment 2, also stimulus complexity was varied by having the participants track short and long words. Stimulus complexity affected tracking: Short words were easier to track than long words. Moreover, familiarity of identity affected tracking of short words (short familiar words were easier to track than short pseudo-words) but not of long words. The results are interpreted within the framework of the model of multiple identity tracking model. Mathematical simulations suggest that observers may not have enough time for full identification of complex identities in a dynamic situation. Practical implications of the results for air traffic control are discussed.     

Models for pedestrian gap acceptance behaviour analysis at unprotected mid-block crosswalks under mixed traffic conditions

Pedestrian safety is an important aspect while crossing the road and it can be explained by pedestrian gap acceptance behaviour. The statistical models such as multiple linear regression (MLR) is often used to model linear relationships between dependent variable (viz., pedestrian gap acceptance behaviour) and independent variables, due to their ability to quantitatively predict the effect of various factors on the dependent variable. However such linear models cannot consider the effect of several variables on the output variable, due to primary assumptions of normality, linear, homoscedasticity and multicollinearity. In this regard, the non-linear models based on the artificial neural network (ANN), which are free from assumptions of linear models, can be easily employed for obtaining the effect of several input variables on the pedestrian accepted gap size. However, researchers have rarely applied ANN modelling technique for predicting the pedestrian gap acceptance behaviour, as the pedestrian gap acceptance behaviour depends on several pedestrian, traffic and vehicular characteristics. The ANN based models would be quite useful in establishing relationship between these factors on the pedestrian gap acceptance behaviour at midblock crosswalks under mixed traffic conditions. In this direction, the present study adopts both MLR as well as ANN with different pedestrian, traffic and vehicular characteristics to assess the significant contributing factors for pedestrians’ gap acceptance behaviour at unprotected mid-block crosswalks under mixed traffic conditions. For this purpose, a video graphic survey was conducted at a six lane divided road at unprotected mid-block crossing in Mumbai, India. The data such as pedestrian (gender and age), vehicular, traffic and pedestrian behavioural characteristics were extracted to model pedestrian accepted gaps. The model results show that pedestrian rolling behaviour has a significant effect on pedestrian accepted gap size. The model results concluded that ANN has a better prediction with possibility to consider the effect of more number of variables on the pedestrian gap acceptance behaviour as compared to the MLR model under mixed traffic conditions. However, the quantification of significant contributing variables on pedestrian accepted gap size is easy by MLR model as compared to the ANN technique. So, both models have their own significant role in pedestrian gap acceptance analysis. The developed models may be useful to enhance the existing mid-block crosswalk facilities or planning new facilities by more accurate prediction of the pedestrian gap acceptance behaviour considering the influence of various factors under mixed traffic conditions.     

Please stop now,automated vehicle! – Passengers aim to avoid risk experiences in interactions with a crossing vulnerable road user at an urban junction

The success of highly automated vehicles (HAVs; SAE Level 4) will depend to a large extent on how well they are accepted by their future passengers. This is especially true for the interaction of these vehicles with other human road users in mixed traffic. In future urban traffic, passengers of HAVs will observe from a passive position how the automated system resolves space-sharing conflicts with crossing vulnerable road users (VRUs; e.g., pedestrians and cyclists) at junctions. For one such crossing-paths conflict, we investigated when passengers would want the HAV to start braking and how much perceived risk passengers accept in the interaction of their vehicle with VRUs. To this end, we conducted 1) an online video study (N = 118), 2) a driving simulator study (N = 28), and 3) a human&vehicle-in-the-loop (Hu&ViL) study at a test site (N = 10). We varied the speed of the HAV (30 km/h vs. 50 km/h), the type (cyclist vs. pedestrian), and crossing direction of the VRU (left vs. right). During the approach to the junction, passengers' task was to trigger the HAV's braking maneuver, in a first trial at the point they considered ideal and in a second trial at the last point they still considered safe enough to decelerate and come to a stop at the stop line. For each braking maneuver, we analyzed the HAV’s distance and time-to-arrival (TTA) to the VRU at braking onset, as well as passengers’ perceived risk in the VRU interaction. The results showed that most passengers preferred harmless interactions with VRUs (at the ideal braking onset time), and accepted unpleasant, but not dangerous interactions at most (at the last acceptable braking onset time). Methodologically, the results were very similar in the three different environments (online, driving simulator, real vehicle). These results clearly show that, in addition to the technical considerations of safe automated driving, passengers’ perception and evaluation of HAV driving behavior should also be taken into account to achieve a satisfying level of acceptance of these vehicles.     

Analysing driver’s decision in dilemma zone at signalized intersections under disordered traffic conditions

Delay in the decision-making process of stop or go during the amber phase of the signal cycle often leads to abrupt hard deceleration or red light violations at signalized intersections. The indecisiveness or the dilemma in decision making often results in compromised safety of the road users. The present study attempts to analyze the driver’s behaviour in order to make the decision of stop or go and developed a binary logistic regression model while considering different traffic behaviour parameters exhibited and observed after the onset of the amber phase. Empirical vehicular trajectory data from three signalized intersections covering 121 signal cycles and 1347 vehicles are used in the study. The study presents two dilemma zone identification models based on distance from the stop line, focusing on easy-to-use and static driver assistance and dynamic-realtime driver assistance systems. Both the models are observed to show good fit and prediction accuracy. The models are validated internally and externally for their adaptability in the field. The effect of different traffic parameters on the dilemma zone is explored, and a possible real-time application of the dynamic model as a driver assistance system in decision-making is explored.     

How do public transport users adjust their travel behaviour if public transport ceases? A qualitative study

Mode shift from public transport (PT) to private car when a PT shut-down occurs results in an increase in the number of car trips on the road network which may contribute to traffic congestion. Policies aimed at reducing the mode shift to car can be designed through a better understanding of PT users’ mode shift in the event of a PT disruption. Furthermore, the share of car mode shift is also an important parameter for assessing traffic congestion relief associated with day to day PT provision in cities. This study sought to uncover factors influencing the mode shift to car among PT users in the event of a PT disruption. Semi-structured interviews were conducted with 30PT users from Melbourne, Australia. Grounded theory was used to derive categories and subcategories of behavioural responses. Factors affecting mode shift to car if PT ceases in the short term were classified into three main themes with several subcategories: individual-specific factors, context-specific factors and journey-specific factors. In the long term, the analysis reveals that only context-specific factors have an influence on mode shift. Findings show that car access, travel time, travel cost, trip importance, non-central business district (CBD) trips, weather, flexibility and accessibility to PT stations are the most important factors favouring use of the car if PT ceases in the short term. This acts to increase traffic congestion due to the mode shift to car. However, in the long term, removing PT is expected to have an impact on land use, leading to individuals changing their residential and workplace location.     

驾驶员选择性注意过程中的规则图式启动效应

本研究将驾驶无意视盲范式与交通违规事件识别任务相结合, 通过两个实验分别探究任务性质(有提示、无提示)和规则图式训练(训练、未训练)对驾驶员识别交通信号和违规事件的影响。结果发现, 交通规则提示能够提高有经验的驾驶员识别交通信号的正确率(实验1); 对于新手驾驶员, 则需要结合规则图式训练才能起到相同效果(实验2)。研究表明驾驶员选择性注意过程中存在规则图式启动效应, 图式训练能够弥补新手驾驶员经验的不足。