A comparative study of flows through funnel-shaped bottlenecks placed in the middle and corner

Upon exiting buildings, theatres, and stadiums, which house a great number of people, egress points can act as bottlenecks, resulting in crowded exits and decreased flows. Most studies investigating flow have been conducted in either narrow bottlenecks (doors) or funnel shape bottlenecks, with the latter investigating bottlenecks placed in the middle of the walkway. This study investigates, for the first time, crowd flow through funnel-shaped bottlenecks placed in the corner of the walkway and makes comparisons with similar bottlenecks of the same length, entrance and exit width placed in the middle of the walkway. The entry width and exit width of the bottlenecks were 3 m and 1 m respectively, with lengths varying from 1 m to 4 m; they continued into a 10 m corridor. Ninety-four participants of various ages were observed moving through each of the configurations. The results indicated that using funnel-shaped bottlenecks in the middle of the walkway increased the flow rate significantly compared to the corner in bottlenecks with 2 m and 3 m lengths. This is contrary to what some other researchers have found for narrow bottlenecks placed in the middle and corner of a wall, although it is recognised that the configuration of funnel-shaped bottlenecks makes the comparison more complex and further work is required in this area. Notwithstanding these results are considered valuable for consideration when designing egress points and corridors in complex buildings such as metro and train stations

Modelling heavy vehicle car-following in congested traffic conditions

Heavy vehicles and passenger cars differ in their manoeuvrability and acceleration capabilities. Heavy vehicles thus influence other traffic in a different manner to passenger vehicles, causing different levels of traffic instability. Increasing number and proportion of heavy vehicles in the traffic stream may result in quite different traffic flow characteristics. Car-following (CF) models are fundamental to replicating traffic flow and thus they have received considerable attention over the last few decades. They are in a continuous state of improvement due to their significant role in traffic micro-simulations, intelligent transportation systems and safety engineering models. However, model estimates of the traffic flow could be degraded since existing CF models do not consider the interactions between these vehicles and passenger cars drivers satisfactorily. This oversight was revealed through a comprehensive literature review conducted in this study in which the existing CF models are classified into classic and artificial intelligence models and are critically reviewed. This research investigates the different car-following behaviour of drivers in congested mixed traffic conditions. The congested traffic conditions refer to the level of service (LOS) “E” and “F” according to the Highway Capacity Manual (HCM 2010). More specifically, this study investigates whether the existence of heavy vehicles in the traffic stream influence car-following behaviour between heavy vehicles and passenger cars? A detailed data analysis is conducted to explore this question using a rich trajectory data set recorded from a 503-meter a segment of freeway in the USA. Four combinations of car-following are considered based on the classes of the vehicles involved in the car-following process. These include heavy vehicle following passenger car (H-C), passenger car following heavy vehicle (C-H), passenger car following passenger car (C-C), and heavy vehicle following heavy vehicle (H-H). This research investigates the headways between the vehicles, driver’s reaction time, relative speed-space headway between the vehicles, and analysis of the vehicle accelerations during car-following process. The study explores the stimuli which can affect driver’s car-following behaviours. It also reconstructs the car-following thresholds for different combinations. The findings showed the fundamental differences amongst the car-following combinations suggesting further investigation and model development. Two CF models are developed in this thesis: one classic model and one artificial intelligence model. This study develops a psychophysical CF model in which four sets of perceptual thresholds are considered to estimate drivers’ car-following behaviour. This means each car-following combination is associated with one specific set of thresholds. The model is calibrated by evolutionary algorithm which is implemented using traffic micro-simulation. A parallel particle swarm optimisation (Parallel PSO) algorithm is implemented in this study to reduce the execution time using multithread methodology. The results show the better performance of the developed model compared to the existing models to estimate traffic measurements used in the traffic micro-simulation. As an alternative model, a new artificial intelligence CF model was developed which specifically considered heavy vehicles. The model used the local linear model tree (LOLIMOT) approach to predict the car-following behaviour of drivers with consideration of the classes of their vehicle and the immediate vehicles in front. The model and the ways of defining the localities and training of the model are explained. The performance of the developed model is evaluated by an independent data set. This evaluation is conducted through the comparison between the predictions of the developed model and the actual traffic measurements. Additionally, the performance for the developed model is compared with the existing CF models. The results showed a good performance of the developed model. This method could be considered as a new approach to modelling car-following behaviour of drivers in mixed traffic providing the opportunity for incorporating human perceptual imperfections into a rigorous modelling framework. This study concludes that the consideration of vehicle heterogeneity in modelling longitudinal behaviours of derivers could result in better representation of traffic flow. This study could be useful for the researchers and transport planners who wish to consider heavy vehicles in traffic stream. The model could be used in traffic micro-simulations to enhance their accuracy and modelling capability

Occupational Health and Job Satisfaction Assessment of Bus Rapid Transit (BRT) Drivers

Several studies have focused on ergonomics of commercial and urban bus drivers; however, there exists a dearth of research on BRT drivers. This study was conducted to investigate the factors affecting the BRT drivers\u27 mental health and satisfaction. The study was carried out on 171 BRT drivers in Tehran, Iran. The required data were collected through two questionnaires. The Classification and Regression Tree (CART) and Hierarchical clustering (HC) was used to extract factors affecting mental health and satisfaction of BRT drivers. The important factors affecting BRT drivers\u27 mental health were: dispute with passengers, depression, BMI, criminal behaviours of passengers, driver\u27s retirement conditions, driver\u27s family conditions, fatigue and the rostering. In addition, the most important factors affecting driver satisfaction were: bus repairs, driver\u27s seat and the sound inside the cabin. Possible practical application includes: creating a counseling and psychotherapy unit and improving the quality of buses and repairment

Impact of driving style, behaviour and anger on crash involvement among Iranian intercity bus drivers

In developing countries such as Iran, due to the inadequate infrastructure for rail and air transportation facilities, intercity buses are the most common type of transportation for long distances. Because of the long hours of driving, bus driving is considered a challenging job. Moreover, given the high capacity of these vehicles, a small error from the driver could endanger many passengers' health. So, studying drivers' behaviours can be a key factor in decreasing the risk factors of crash involvement in these drivers. However, few studies have focused on intercity bus drivers' behaviours. This research uses a sample of 254 professional drivers that answered a self-report questionnaire on driving style (MDSI), driving behaviour (DBQ), and driving anger (DAS). A structural equation modelling (SEM) is used to investigate the psychometric properties of these questionnaires. The results show a positive correlation between maladaptive driving styles and driving behaviour, and a negative correlation between adaptive styles and driving behaviour. Significant differences are observed among drivers with and without crash history on their maladaptive driving styles and their driving anger scale. A binary logistic regression model is also developed to predict traffic crashes as a function of driving misbehaviour. The results suggest that factors related to driving anger are the main factors that increase the probability of misbehaviour and traffic crashes. The results also suggest that driving style and driving behaviour significantly predict crash risk among bus drivers. Aggressive driving is associated with an increased probability of crash involvement among intercity bus drivers. The findings can be used to inform the health promotion policies and provide regular interventions designed to improve driving safety among intercity bus drivers

The Role of Big Five Personality Traits in Explaining Pedestrian Anger Expression

Although the relationship between anger and personality characteristics in the literature is well-acknowledged for drivers, there is a lack of systematic investigation of pedestrians. The current study aimed to evaluate pedestrian anger expression (PAX) and its contributing factors, including demographics, travel habits, and the big five personality traits. To test the effects of different variables on PAX scales, data from 742 respondents were collected. The data were analyzed through a two-stage approach of clustering and a logistic regression model. Participants were clustered into two groups of low expression and high expression based on their responses to PAX items. An exploratory factor analysis identified significant constructs of PAX, including “Adaptive/Constructive Expression”, “Anger Expression-In”, and “Anger Expression-out”. It was found that males were more likely to show high anger expressions. Public transport usage and previous crash involvement could significantly increase the probability of high anger expression. On the other hand, life satisfaction and intention to avoid traffic were negatively associated with high anger expression. The results revealed that neuroticism, extraversion, and openness to experience could positively contribute to higher anger expression; however, agreeableness and conscientiousness were negatively associated with high anger expression for pedestrians

Investigating Pedestrians’ Exit Choice with Incident Location Awareness in an Emergency in a Multi-Level Shopping Complex

Limited studies have investigated pedestrians’ exit choices in an emergency in multi-level commercial buildings. In particular, the comparison between exit choices before and after awareness of an incident location is non-existent in the literature. Likewise, the influence of individual attributes, such as the presence of a child or a companion, on the individual’s exit choice in complex architectural layouts has rarely been studied in the literature. This paper aims to address these knowledge gaps by investigating pedestrians’ exit choice behavior in an emergency at a multi-level shopping complex considering exit choice behavior before and after awareness of incident location and the influence of personal attributes (e.g., presence of a child or companion). A survey of 1271 pedestrians for two hypothetical emergency scenarios in a multi-level shopping center in Tehran, Iran was conducted. A tablet-based simulator of a multi-story commercial complex was designed, and on-site interviews were conducted. In the first scenario, participants were asked to select their preferred exit door at the start of the emergency alarm without being informed about the incident location. In the next scenario, the scene of an incident (fire) was displayed without altering the conditions, and pedestrians were asked to choose their desired exit. The utility models investigated the differences in pedestrians’ behavior before and after awareness of the fire location. The models show differences in pedestrian decisions to evacuate and select the exit when the fire location information was available compared to when only emergency alarm information was available. Further, differences in evacuation strategy between the people who preferred to delay the exit and those who preferred to exit immediately were observed. Participants with children were more concerned about the ease of moving on the route and preferred a less congested route and exit area. Differences in evacuation behavior on the ground floor and other floors were also observed

Modelling heavy vehicle car-following in congested traffic conditions

Heavy vehicles and passenger cars differ in their manoeuvrability and acceleration capabilities. Heavy vehicles thus influence other traffic in a different manner to passenger vehicles, causing different levels of traffic instability. Increasing number and proportion of heavy vehicles in the traffic stream may result in quite different traffic flow characteristics. Car-following (CF) models are fundamental to replicating traffic flow and thus they have received considerable attention over the last few decades. They are in a continuous state of improvement due to their significant role in traffic micro-simulations, intelligent transportation systems and safety engineering models. However, model estimates of the traffic flow could be degraded since existing CF models do not consider the interactions between these vehicles and passenger cars drivers satisfactorily. This oversight was revealed through a comprehensive literature review conducted in this study in which the existing CF models are classified into classic and artificial intelligence models and are critically reviewed. This research investigates the different car-following behaviour of drivers in congested mixed traffic conditions. The congested traffic conditions refer to the level of service (LOS) “E” and “F” according to the Highway Capacity Manual (HCM 2010). More specifically, this study investigates whether the existence of heavy vehicles in the traffic stream influence car-following behaviour between heavy vehicles and passenger cars? A detailed data analysis is conducted to explore this question using a rich trajectory data set recorded from a 503-meter a segment of freeway in the USA. Four combinations of car-following are considered based on the classes of the vehicles involved in the car-following process. These include heavy vehicle following passenger car (H-C), passenger car following heavy vehicle (C-H), passenger car following passenger car (C-C), and heavy vehicle following heavy vehicle (H-H). This research investigates the headways between the vehicles, driver’s reaction time, relative speed-space headway between the vehicles, and analysis of the vehicle accelerations during car-following process. The study explores the stimuli which can affect driver’s car-following behaviours. It also reconstructs the car-following thresholds for different combinations. The findings showed the fundamental differences amongst the car-following combinations suggesting further investigation and model development. Two CF models are developed in this thesis: one classic model and one artificial intelligence model. This study develops a psychophysical CF model in which four sets of perceptual thresholds are considered to estimate drivers’ car-following behaviour. This means each car-following combination is associated with one specific set of thresholds. The model is calibrated by evolutionary algorithm which is implemented using traffic micro-simulation. A parallel particle swarm optimisation (Parallel PSO) algorithm is implemented in this study to reduce the execution time using multithread methodology. The results show the better performance of the developed model compared to the existing models to estimate traffic measurements used in the traffic micro-simulation. As an alternative model, a new artificial intelligence CF model was developed which specifically considered heavy vehicles. The model used the local linear model tree (LOLIMOT) approach to predict the car-following behaviour of drivers with consideration of the classes of their vehicle and the immediate vehicles in front. The model and the ways of defining the localities and training of the model are explained. The performance of the developed model is evaluated by an independent data set. This evaluation is conducted through the comparison between the predictions of the developed model and the actual traffic measurements. Additionally, the performance for the developed model is compared with the existing CF models. The results showed a good performance of the developed model. This method could be considered as a new approach to modelling car-following behaviour of drivers in mixed traffic providing the opportunity for incorporating human perceptual imperfections into a rigorous modelling framework. This study concludes that the consideration of vehicle heterogeneity in modelling longitudinal behaviours of derivers could result in better representation of traffic flow. This study could be useful for the researchers and transport planners who wish to consider heavy vehicles in traffic stream. The model could be used in traffic micro-simulations to enhance their accuracy and modelling capability

Effect of Intersecting Angle on Pedestrian Crowd Flow under Normal and Evacuation Conditions

Complex pedestrian or passenger crowd movements, such as intersecting movements, can create a bottleneck resulting in delays during emergency escape from public infrastructure such as major public transport hubs. Limited studies have examined the effect of different intersecting angles and walking speeds on pedestrian outflow. This study aims to systematically investigate the effect of different intersecting angles (30°, 90°, and 150°) and walking speeds (normal walking, faster walking) on pedestrian outflow at an intersecting path or junction through controlled laboratory experiments. Further, we consider both blocked vision and un-blocked vision in our experiments. The results from our experiments show that the acute angle of 30° has a higher flow rate and less evacuation time as compared to the other angles. The obtuse intersecting angle of 150° was the most undesirable intersecting angle in terms of outflow, evacuation time, and delays at the junction. Faster walking generally led to reduced evacuation time as compared to normal walking. It is also interesting to note that the results from both blocked vision and un-blocked vision were not statistically significant, suggesting that line of sight was not an important factor in regulating the flow at the junction. The results from our findings are a valuable resource to verify the mathematical model intended to simulate pedestrian or passenger crowd movements and behavior within major public infrastructure under both normal and evacuation conditions

The Role of Big Five Personality Traits in Explaining Pedestrian Anger Expression

Although the relationship between anger and personality characteristics in the literature is well-acknowledged for drivers, there is a lack of systematic investigation of pedestrians. The current study aimed to evaluate pedestrian anger expression (PAX) and its contributing factors, including demographics, travel habits, and the big five personality traits. To test the effects of different variables on PAX scales, data from 742 respondents were collected. The data were analyzed through a two-stage approach of clustering and a logistic regression model. Participants were clustered into two groups of low expression and high expression based on their responses to PAX items. An exploratory factor analysis identified significant constructs of PAX, including “Adaptive/Constructive Expression”, “Anger Expression-In”, and “Anger Expression-out”. It was found that males were more likely to show high anger expressions. Public transport usage and previous crash involvement could significantly increase the probability of high anger expression. On the other hand, life satisfaction and intention to avoid traffic were negatively associated with high anger expression. The results revealed that neuroticism, extraversion, and openness to experience could positively contribute to higher anger expression; however, agreeableness and conscientiousness were negatively associated with high anger expression for pedestrians