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Professor Fornasier and his team have recently proven mathematical statements that demonstrate how surprisingly easy it is to automatically generate precise models for specific, relatively simple group interactions based on observed dynamics data. Using computer simulations, the mathematicians can describe potential collective behavioral patterns of a large number of individuals who mutually influence each other in a given situation.

In an experiment conducted in May in collaboration with Consiglio Nazionale delle Ricerche CNR and the University of Rome "La Sapienza" in Italy, Fornasier and his team demonstrated that the process is in fact amenable to influencing group behavior.

Human factors reflection on existing traffic management measures | SWOV

To this end, the researchers assigned two groups of 40 students each the task of finding a specific location in a building. The scientists planted two incognito informed agents into one of the groups. By merely moving very determinedly in a predefined direction, the agents were able to steer the group toward the target spot. This experiment demonstrates that taking control of self-organizing systems, which also include groups of individuals, is possible with surprisingly little effort. The mathematicians also confirmed that the results apply equally well to very large groups. The fact that his mathematical models are formulated in an entirely abstract environment makes them easily adaptable to a wide variety of situations.

This facilitates finding efficient solutions for steering large masses of people though buildings in a stress-free manner or evacuating people in emergency situations. There, precisely coordinated activities by big investors can result in sizable market movements. Emerald, UK, ISBN: In this paper, some basic concepts of pro-social behaviour are illustrated.

The sensitivity of the social equilibrium to social values is investigated. Also introduced here is a dynamic travel-choice model of travel behaviour which considers social value orientation. The potential for incorporating social aspects in the development of transport modelling is demonstrated by a numeric example. Such models include the mathematical and logical abstractions of real-world systems implemented in computer software.

In many of the applied models each traveller is formulated as an individual agent, making independent decisions about his or her desired use of the transport system travel mode, route, departure time, etc. Social aspects of travel behaviour such as social value orientation are commonly omitted from the formal modelling process, quite often treated as unbiased random errors or qualitative caveats.

Travel behaviour research in recent years has tended to focus on normative models, which tend to represent the individual traveller as a homo economicus, a rational economic human being, rather than on descriptive models to represent and measure travel behaviour without making an explicit value judgment. A clear distinction between normative modelling and descriptive modelling of travel behaviour is not always made. Many transport problems can be defined as social dilemmas. A social dilemma problem represents a situation in which voluntary contributions are needed to attain some common and shared social payoff, and where the rational choice of the individual is to not to cooperate.

It is common to represent the performance of a traffic network as the aggregate behaviour of the individual agents, not taking into consideration the social interactions and the social values they may have towards each other. This approach is based on an implicit assumption that social aspects can be neglected. Recently, there have been signs of increased interest in the study of social influence in the context of travel, mainly in activity-based modelling see, for examples, Vovsha et al.

However, socio-psychological aspects of dynamic choice behaviour have not gained much attention from researchers of the more traditional travel behaviour models such as equilibrium models, microsimulation, and discrete choice analysis. This paper presents an investigation of the effect of social value orientation on choice behaviour in a path-choice situation, simulating a social dilemma.

Under System Optimum SO conditions traffic should be arranged in congested networks such that the total travel cost is minimised.


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However, in such networks, patterns of traffic flow may differ from the socially efficient state of system optimum, as individual travellers attempt to minimize their own travel cost without taking into consideration the effects of their actions on other travellers, thus without considering the system externalities.

A typical equilibrium of a traffic network with a finite number of non-cooperative agents players is the Nash non-cooperative optimum. When players are symmetric i.

Estimation of traffic flow changes using networks in networks approaches

The traffic flows that satisfy this principle are usually referred to as User Equilibrium UE flows, since each user chooses the route that is the best for him or herself. This well-known equilibrium became accepted by transport modellers as a sound and simple behavioural principle to describe the spreading of trips over alternate routes due to congested conditions.

While in most of the transport applications, we are interested in studying the behaviour of the totalistic system as the prime focus, the tools used by transport modellers tend to focus on the behaviour of the individual traveller. The analysis of travel behaviour is typically disaggregated, meaning that common models represent the choice behaviour of individual decision-making entities, whether these are individual travellers or households. However, merely aggregating individuals' choices means that the functions and the characteristics of the social system are ignored.

Attention should be given to interactions between individuals who are part of a social system, and to other social aspects of travel behaviour that may influence the system equilibrium and the system dynamics. Assuming travellers behave in a completely non-cooperative and selfish way might be too extreme.

The importance of understanding the social aspects of travel-choice behaviour is not only relevant to the measurement and the prediction of such behaviour. It may also be important in terms of influencing and changing travel behaviour. Many definitions of altruism also include what is often considered a critical component of such behaviour: that the behaviour must have some cost for the actor.

According to Sorrentino and Rushton , p. Fehr and Fischbacher distinguish between reciprocal altruism, whereby people help in return for having been helped, and strong reciprocity. They define strong reciprocity as a combination of altruistic rewarding and altruistic punishment. Strong reciprocators bear the cost of rewarding cooperators or punishing defectors even it confers no personal benefit, whereas reciprocal altruists only reward or punish if this is in their long-term self-interest.

Many behavioural scientists debate the existence of pure altruism in humans Skinner For example, where gains to the beneficiary not perceived to be meaningfully larger than the costs to the benefactor, cooperative players may not be regarded as altruistic. This work looks at pro-social behaviour which is a broader term than altruism. It comprises helpful actions intended to benefit another person, which are not undertaken through professional obligation. Pro-social behaviour can be categorised as either egoistically motivated helping someone in order, ultimately, to benefit oneself or altruistically motivated intended only to benefit the other person Bierhoff, One of the factors determining whether or not such a transformation takes place is social value orientation McClintock, A distinction is made between prosocials and proselves is made in the study of social dilemmas See, for examples, Van Vugt et al.

Over the last decade, the study of social interactions, social value orientation and collaborative behaviour has attracted much research in behavioural sciences and economic decision-making see a review in Soetevent, Structural interventions can alter the objective features of the decision situation by changing the incentive patterns associated with cooperation and non-cooperation See, for example, Yamagishi, Providers and managers of transport systems have introduced structural interventions that include a change of the incentive patterns associated with cooperation and non-cooperation.

Typical examples of such interventions may include changing the payoff structure e. Recently, there has also been increasing interest in the influence of psychological and social aspects on the behaviour of travellers. Sunitiyoso et al. Goulias and Henson considered pro-social behaviour and altruism as a powerful determinant of travel behaviour, and as a motivator to use in changing travel behaviour. They provide two main reasons to study the potential of altruistic behaviour modelling in such a context: a understand altruism as a value to use in motivating people to move toward the common good; and b understand altruism expressed in specific activity and travel behaviours.

They suggest that social interactions should be the core of activity-based approaches to travel demand forecasting. Laboratory experiments simulating route-choice situations Rapoport et al. In all of the above works, participants were not familiar with each other, communication between participants during the experiment was forbidden and the participants were paid based on their individual performance.

Morgan et al. One may argue that these aspects of experimental design do not encourage participants to exhibit prosocial value orientation, and that there is not much reason to expect prosocial behaviour by the participants. Other important factors that might influence the degree of prosocial value orientation may be the size and complexity of the transport network e. A small group of individuals is more likely to secure voluntary compliance than a larger group Olson, One may argue that due to the social characteristics of the traffic situations which travellers are faced with, it is unlikely that common users of the traffic network exhibit strong pro-social behaviour.

Indeed, experimental work on route-choice Rapoport et al.

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The translation of social responsibility to economic behaviour can be done by adding the attitude toward the policy or the community to the utility function See Train et al. Following this concept, an n-agents system in which social values influence travel choice, is considered. Agent i's social utility at time period t is defined as follows: n n Ui x t k ii tt i x t k ij tt j x t ; k ii k ij 1 1 i j i j The first component of Eq. The weighted utilities of other agents, in the mind of agent i, are represented by the second component of Eq.

Other externalities, related to the travel choices made by the network users, are not explicitly represented in Eq. However, in some contexts of travel behaviour mainly car driving we may find some evidence to users who fail to acknowledge the courtesy of others, aggressive driving, road rage, and even physical violence among travellers.

There are two applications of Eq. This is demonstrated by the numeric example given in the next section. In section 3. The Social Value User Equilibrium In order to illustrate the traffic assignment process, and to demonstrate some of the concerns related to the choice of scale when representing a traffic network, the following numeric problem is considered. Let us consider a simple network problem, presented in Figure 1. This situation happens because the users of the network users do not face the true social cost of an action; in a situation where all travellers exhibit pro-social travel behaviour no traffic is assigned to path 3.

The user equilibrium and the system optimum are not the only possible equilibrium states, and other social equilibrium states may be presence as well. On the other hand, motivating agents with higher social values may not make much impact on the resulting social equilibrium and the system efficiency. Experiment Based on the numeric example described in section 3. The participants knew each other before the experiment.

They had basic background in operational research, but have never been introduced before to concepts of network theory or concepts of equilibrium. The participants were introduced to the simple network problem shown in Figure 1, and were provided with the functions to calculate the different path travel times. On each trial, each participant was asked to choose one of the two alternative paths.

The participants were given about 30 seconds to write down their choices. They were not allowed to discuss their choices with their colleagues or inform them of their decision. After all the participants made their choices, they were provided with the travel time on each of the paths. Following this information, they were asked to make another choice.


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This stage was repeated four times. The experimental results do not provide evidence in support for the existence and significance of pro-social behaviour. But they do not necessarily support the opposite, but they are close to the predictions of user equilibrium. However, one should be careful in explaining the experimental results by the existence of pro-social values: it is rather likely that some of the deviation from the system optimum is due to confusion, inexperience with the network, and the small number of iterations.