Using BDI agents to improve driver modelling in a commuter scenario

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents.     

Decision support for simulating the car park activity in an urban area

Complexity of car park activity is reproduced from a concurrent execution of behaviour of various drivers. This paper presents a step in the development of a multimodal traffic simulator based on multi‐agent paradigm and designed as a decision aid tool as well as a video game. The user‐player has the opportunity to test different scenarios. We propose an approach for designing the decision‐making rules and the learning mechanism for a car driver agent. For that, a panel of methods such as stated preference modelling, Design Of Experiments and data fusion is used. Initial behavioural models, based on similar preferences, are developed for specified categories. Each agent will adapt its behaviour after executing its learning process. Our approach can be used in order to optimize needs of road network users and those of people in charge of traffic regulation. A demonstrator has been developed to test parking policies in an urban area as well as changes of car park characteristics.     

A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.     

Route Choice Models Used in the Stochastic User Equilibrium Problem: A Review

Several route choice models are reviewed in the context of the stochastic user equilibrium problem. The traffic assignment problem has been extensively studied in the literature. Several models were developed focusing mainly on the solution of the link flow pattern for congested urban areas. The behavioural assumption governing route choice, which is the essential part of any traffic assignment model, received relatively much less attention. The core of any traffic assignment method is the route choice model. In the wellknown deterministic case, a simple choice model is assumed in which drivers choose their best route. The assumption of perfect knowledge of travel costs has been long considered inadequate to explain travel behaviour. Consequently, probabilistic route choice models were developed in which drivers were assumed to minimize their perceived costs given a set of routes. The objective of the paper is to review the different route choice models used to solve the traffic assignment problem. Focus is on the different model structures. The paper connects some of the route choice models proposed long ago, such as the logit and probit models, with recently developed models. It discusses several extensions to the simple logit model, as well as the choice set generation problem and the incorporation of the models in the assignment problem.     

Modeling the effects of traveler information on freeway origin–destination demand prediction

The primary focus of this research is to develop an approach to capture the effect of travel time information on travelers’ route switching behavior in real-time, based on on-line traffic surveillance data. It also presents a freeway Origin–Destination demand prediction algorithm using an adaptive Kalman Filtering technique, where the effect of travel time information on users’ route diversion behavior has been explicitly modeled using a dynamic, aggregate, route diversion model. The inherent dynamic nature of the traffic flow characteristics is captured using a Kalman Filter modeling framework. Changes in drivers’ perceptions, as well as other randomness in the route diversion behavior, have been modeled using an adaptive, aggregate, dynamic linear model where the model parameters are updated on-line using a Bayesian updating approach. The impact of route diversion on freeway Origin–Destination demands has been integrated in the estimation framework. The proposed methodology is evaluated using data obtained from a microscopic traffic simulator, INTEGRATION. Experimental results on a freeway corridor in northwest Indiana establish that significant improvement in Origin–Destination demand prediction can be achieved by explicitly accounting for route diversion behavior.     

Behavioural intentions as predictors of very specific behaviour

This paper proposes an alternative approach to understanding travel behaviour —the study of behavioural intentions. The communality of this and the revealed behaviour approach are outlined and the use of controlled experimental methods illustrated in an analysis of empirical data on behavioural intentions of citizens with respect to changes in the levels of transport service, and the introduction of an area licensing scheme for travel in a central city area in Australia.This research was completed while Jordan Louviere was at Cambridge Systematics Inc., Boston, Massachusetts, U.S.A.     

A recursive, cell processing model for predicting freeway travel times

Travel time information influences driver behaviour and can contribute to reducing congestion and improving network efficiency. Consequently many road authorities disseminate travel time information on road side signs, web sites and radio traffic broadcasts. Operational systems commonly rely on speed data obtained from inductive loop detectors and estimate travel times using simple algorithms that are known to provide poor predictions particularly on either side of the peak period. This paper presents a new macroscopic model for predicting freeway travel times which overcomes the limitations of operational ‘instantaneous’ speed models by drawing on queuing theory to model the processing of vehicles in sections or cells of the freeway. The model draws on real-time speed, flow and occupancy data and is formulated to accommodate varying geometric conditions, the relative distribution of vehicles along the freeway, variations in speed limits, the impact of ramp flows and fixed or transient bottlenecks. Field validation of the new algorithm was undertaken using data from two operational freeways in Melbourne, Australia. Consistent with the results of simulation testing, the validation confirmed that the recursive model provided a substantial improvement in travel time predictions when compared to the model currently used to provide real-time travel time information to motorists in Melbourne.     

Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging.     

Accuracy of the Gothenburg congestion charges forecast

This paper explores the accuracy of the transport model forecast of the Gothenburg congestion charges, implemented in 2013. The design of the charging system implies that the path disutility cannot be computed as a sum of link attributes. The route choice model is therefore implemented as a hierarchical algorithm, applying a continuous value of travel time (VTT) distribution. The VTT distribution was estimated from stated choice (SC) data. However, based on experience of impact forecasting with a similar model and of impact outcome of congestion charges in Stockholm, the estimated VTT distribution had to be stretched to the right. We find that the forecast traffic reductions across the cordon and travel time gains were close to those observed in the peak. However, the reduction in traffic across the cordon was underpredicted off-peak. The necessity to make the adjustment indicates that the VTT inferred from SC data does not reveal the travellers’ preferences, or that there are factors determining route choice other than those included in the model: travel distance, travel time and congestion charge.     

Validating the results of a route choice simulator

This paper describes the validation of a route choice simulator known as VLADIMIR (Variable Legend Assessment Device for Interactive Measurement of Individual Route choice). VLADIMIR is an interactive computer-based tool designed to study drivers’ route choice behaviour. It has been extensively used to obtain data on route choice in the presence of information sources such as Variable Message Signs or In-Car Navigation devices. The simulator uses a sequence of digitized photographs to portray a real network with junctions, links, landmarks and road signs. Subject drivers are invited to make journeys between specified origins and destinations under a range of travel scenarios, during which the simulator automatically records their route choices. This paper describes validation experiments carried out during the period Summer 1994 to Autumn 1995 and reports on the results obtained. Each experiment involved a comparison of routes selected in real life with those driven under simulated conditions in VLADIMIR. The analysis included investigation of the subjects’ own assessment of the realism of the VLADIMIR routes they had chosen, a comparison of models based on the real life routes with models based on VLADIMIR routes, and a statistical comparison of the two sets of routes. After an extensive series of data collection exercises and analyses, we have concluded that a well designed simulator is able to replicate real life route choices with a very high degree of detail and accuracy. Not only was VLADIMIR able to precisely replicate the route choices of drivers who were familiar with the network but it also appears capable of representing the kind of errors made and route choice strategies adopted by less familiar drivers. Furthermore, evidence is presented to suggest that it can accurately replicate route choice responses to roadside VMS information.     

Vehicle routing problem and driver behaviour: a review and framework for analysis

Vehicle routing problems (VRPs) whose typical objective is to minimise total travel costs over a tour have evolved over the years with objectives ranging from minimising travel times and distances to minimising pollution and fuel consumption. However, driver behaviour continues to be neglected while planning for vehicle routes. Factors such as traffic congestion levels, monotonous drives and fatigue have an impact on the behaviour of drivers, which in turn might affect their speed-choice and route-choice behaviours. The behaviour of drivers and their subsequent decision-making owing to these factors impact the revenue of transport companies and could lead to huge losses in extreme cases. There have been studies on the behaviour of drivers in isolation, without inclusion of the objectives and constraints of the traditional routing problem. This paper presents a review of existing models of VRP, planner behaviour models in the VRP context and driver behaviour models and provides a motivation to integrate these models in a stochastic traffic environment to produce practical, economic and driver-friendly logistics solutions. The paper provides valuable insights on the relevance of behavioural issues in logistics and highlights the modelling implications of incorporating planner and driver behaviour in the framework of routing problems.     

Driver requirements and road traffic informatics

Initially the driver's role as a link in the driver-vehicle-road-traffic control-chain is discussed in a historical perspective. The gradual changes and the advantages and problems arising from these changes are discussed from behavioural point of view.Then the driver tasks are analyzed. A separation is made between trip planning, navigation, road following, traffic interaction, rule compliance, other than traffic tasks, car handling and speed choice. The relations between and the weights of these subtasks are discussed. Some existing driver behaviour models are reviewed in relation to the above mentioned tasks.Finally an effort is made based on the analyses of driver tasks and driver models to specify some general and some more specific potential advantages and problems with expected future RTI-systems.     

The call of the road: factors predicting students’ car travelling intentions and behaviour

The most common daily trip for employed persons and students is the commute to and from work and/or place of study. Though there are clear environmental, health and safety benefits from using public transport instead of private vehicles for these trips, a high proportion of commuters still choose private vehicles to get to work or study. This study reports an investigation of psychological factors influencing students’ travel choices from the perspective of the Theory of Planned Behaviour (TPB). Students from three different university campuses (n = 186) completed a cross-sectional survey on their car commuting behaviour. Particular focus was given to whether car commuting habits could add to understanding of commuting behaviour over and above behavioural intentions. Results indicated that, as expected, behavioural intention to travel by car was the strongest TPB predictor of car commuting behaviour. Further, general car commuting habits explained additional variance over and above TPB constructs, though the contribution was modest. No relationship between habit and intentions was found. Overall results suggest that, although student car commuting behaviour is habitual in nature, it is predominantly guided by reasoned action. Implications of these findings are that in order to alter the use of private vehicles, the factors influencing commuters’ intentions to travel by car must be addressed. Specifically, interventions should target the perceived high levels of both the acceptability of commuting by car and the perceived control over travel undertaken by private vehicle.     

Behavioural implications of preferences, risk attitudes and beliefs in modelling risky travel choice with travel time variability

The appropriate interpretation of a behavioural outcome requires allowing for risk attitude and belief of an individual, in addition to identification of preferences. This paper develops an Attribute-Specific Extended Rank-Dependent Utility Theory model to better understand choice behaviour in the presence of travel time variability, in which these three important components of choice are empirically addressed. This framework is more behaviourally appealing for travel time and travel time variability research than the traditional approach in which risk attitude and belief are overlooked. This model also reveals significant unobserved between-individual heterogeneity in preferences, risk attitudes and beliefs.     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

The acquisition of pre-trip information: A stated preference approach

This paper describes a study into the effects of pre-trip information on travel behaviour, carried out as part of the DRIVE project EURONETT. The aim of the study was to investigate travellers' requirements for different types of travel information and methods of enquiry and to relate the process of information acquisition to changes in travel behaviour. The study was carried out using a stated preference approach, built on the use of a microcomputer based simulation of an in-home pre-trip information system offering information on travel times from home to City Centre, by bus and car, at different times of the day. A novel feature of the stated preference exercise was that respondents effectively generated their own choice set of alternatives through the process of information acquisition. Surveys were undertaken in parallel in Birmingham and Athens, thus allowing a comparison to be made between behaviour in typical Southern and Northern European settings.The first part of the paper discusses some of the fundamental behavioural and modelling issues raised by the introduction of advanced traveller information systems. It then describes the study methodology and the stated preference experiment. Results are presented from an analysis of the information acquisition process itself and from choice models relating the acquired information to effects on different dimensions of travel behaviour.     

Fuzzy preference based traffic assignment problem

This paper introduces a fuzzy preference based model of route choice. The core of the model is FiPV (Fuzzy individuelle Präferenzen von Verkehrsteilnehmern or fuzzy traveler preferences), that is a choice function based on fuzzy preference relations for travel decisions. The proposed model may be the first application of fuzzy individual choice in traffic assignment and probably also the first in this class to consider the spatial knowledge of individual travelers. It is argued that travelers do not or cannot always follow the maximization principle. Therefore we formulate a model that also takes into account the travelers with non-maximizing behavior. The model is based on fuzzy preference relations, of which elements are fuzzy pairwise comparisons between the available alternatives.     

Stated preferences for investigating commuters' diversion propensity

A reasonable response to increasing traffic congestion may come from the rapidly developing traveler information systems. Such systems may be successful if they effectively influence drivers' enroute decisions; in this regard, a critical factor may be commuters' willingness to divert from their regular route in response to information about traffic congestion. This study evaluates the effects of real-time traffic information along with driver attributes, roadway characteristics and situational factors on drivers' willingness to divert.The empirical portion of this study is based on a survey of downtown Chicago automobile commuters. The stated preference approach was used to study commuters' diversion propensity. Drivers expressed a higher willingness to divert if expected delays on their usual route increased, if the congestion was incident-induced as opposed to recurring, if delay information was received from radio traffic reports compared with observing congestion, and if trip direction was home-to-work rather than work-to-home. Respondents were less willing to divert if their alternate route was unfamiliar, unsafe or had several traffic stops. Socioeconomic characteristics were also significant in predicting willingness to divert.     

Energy and emissions impacts of a freeway-based dynamic eco-driving system

Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US CO₂ emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO₂ emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person’s driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle’s vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10–20% in fuel savings and lower CO₂ emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO₂ emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.     

Applying behavioural theories to studying the influence of climate change on young people’s future travel intentions

Transport policy in the UK is seeking to promote the development of low carbon transport technology and to encourage people to choose to use low carbon travel options. This paper draws on existing behavioural theories to study young people’s travel behaviour intentions and the influence on these from their knowledge of, and willingness to act on, climate change. The study involved a series of focus groups with young people aged 11-18 years, where attitudes to transport modes, attitudes towards climate change and travel behaviour intentions were discussed. Knowledge and values are established as the key determinants of young people’s attitudes and behaviour intentions towards transport in the context of climate change. More specifically it is established that young people’s values emphasise speed and freedom and that it is important to young people that the mode of transport they choose is reflective of the image they want to portray.