Positive Model of Departure Time Choice Under Road Pricing and Uncertainty (original) (raw)
Related papers
1999
This paper explores a methodology widely used in economics but rather seldom used in the field of transport economics: Experimental economics. The impact of traffic stochasticity on commuters' behaviour is investigated using standard decision theory under uncertainty. A simple model of route and departure time choices within a stochastic traffic environment is designed. This model provides a basis for a computer-led experiment that was conducted in a preliminary phase of this work. Though at this stage the model seems too straight forward to fit the data, this methodology seems promising to investigate the behavioural impact of various information and pricing environments. (A) For the covering abstract see ITRD E105186.
Availability of Information and Dynamics of Departure Time Choice: Experimental Investigation
Transportation Research Record, 1986
Abstract: The effect of information availability on the dynamics of user behavior in urban commuting systems is investigated through an experimental procedure that involves real commuters interacting in a simulated traffic system under two distinct informational situations: in one only the decision maker's own performance on the previous day is available, and in the other complete information about the system's performance on the previous day is available. The results are examined from the perspective of a theoretical ...
Experiments with departure time choice dynamics of urban commuters
Transportation Research Part B-methodological, 1986
A modeling framework is presented for investigating the dynamic properties of a system of commuters located along a highway facility. The day-to-day evolution of the time-dependent user departure patterns and associated system performance are investigated in a series of simulation experiments. User departure time decisions are modeled through the use of simple heuristics, including mechanisms to incorporate the experience accumulated through repeated use of the facility. The performance of the facility given the users' decisions is obtained from a special-purpose macroscopic traffic simulation model. Alternative user choice rules and learning models are explored as to their effect on system performance and its dynamic properties.
Travel time saving plays an important role in the socio-economic studies of the profitability of transport projects. This paper presents some parts of our studies performed in collaboration with SETRA (French Ministry of Transport) on the economic evaluation of operating roads. The present paper includes four main parts. In the first section, we describe how the decisions of travelers are influenced by the variability of travel time. The statistical analysis of the recorded travel time on a motorway in Île-de-France is developed to quantify the variability of travel time and to measure its magnitude. The second section is devoted to developing the methodological framework to capture the safety margin considered by travelers in their trip making behaviors. A model is formulated to reflect the behavior of the traveler before making a trip. This model highlights the effect of each travel component, the mean of the travel time, the level of variability, and the safety margin taken by the traveler before traveling. The third section extracts the optimal safety margin that minimizes the cost of the total time allocated to travel. Finally, the last section of this paper is devoted to some algebraic manipulation in order to characterize the features of the output if the variability of travel time in the economic evaluation of transport projects is incorporated. An expression is also provided for the gain brought by operators when they reduce the variability of travel time or they reduce the uncertainty felt by travelers through providing information to them.
Implementation of Departure Time and Mode Choice Model with Travel Time Uncertainty
Congestion is a major problem in many large cities today. It increases air pollution, travel times and travel time uncertainty. City governments introduce policy measures in order to reduce congestion. In Stockholm, a trial with congestion charges has recently been carried out (January 3rd to July 31st, 2006) ). The aim of the trial was to evaluate if road tolls can reduce over-all traffic in the city-centre and also if it can achieve a more efficient usage of the transportation system. To achieve a peak-spreading effect, the congestion charges were time-of-day dependent and higher in the peak hours.
A micro-simulation model system of departure time and route choice under travel time uncertainty
2000
Existing microscopic traffic models have often neglected departure time change as a possible response to congestion. In addition, they lack a formal model of how travellers base their daily travel decisions on the accumulated experience gathered from repetitively travelling through the transport network. This paper proposes an approach to account for these shortcomings. A micro-simulation approach is applied, in which individuals base their consecutive departure time decisions on a mental model. The mental model is the outcome of a continuous process of perception updating according to reinforcement learning principles. The individuals are linked to the traffic simulator SIAS-PARAMICS to create a simulation system in which both individual decision-making and system performance (and interactions between these two levels) are adequately represented. The model is applied in a case study that supports the feasibility of this approach.
Behavioural Responses and Network Effects of Time-varying Road Pricing
2009
Road pricing is a policy measure under consideration by many goverments and road authorities. Although objectives may be different any road pricing measure will impact the behaviour of travellers and the flow of traffic. In this research we specifically looked at the effects of road pricing measures where the prices are differentiated by time and place. Different choice models were estimated using data from a stated preference survey that was designed within this research. We find that the sensitivities of commuters towards changing departure time exist for both departure and arrival time, they are non-linear and they depend on personal constraints. Also the unreliability in travel time affects the departure time choice of commuters. Since the sensitivities towards rescheduling are high, it is to be recommended to introduce supporting policy measure that lower sensitivities towards rescheduling. In this research we also developed a dynamic modelling framework that can forecast the t...
Application of Stochastic Learning Automata for Modeling Departure Time and Route Choice Behavior
Transportation Research Record, 2002
This paper uses Stochastic Learning Automata (SLA) theory to model the learning behavior of commuters within the context of the combined departure time route choice (CDTRC) problem. The SLA model uses a reinforcement scheme to model the learning behavior of drivers. In this paper, a multiaction Linear Reward-Penalty reinforcement scheme is introduced to model the learning behavior of travelers based on past departure time choice and route choice. In order to test the model, a traffic simulation is developed. The results of the simulation are intended to show that the drivers learn the best CDTRC option, and that the network achieves user equilibrium in the long run. Results evidence that the developed SLA model accurately portrays the learning behavior of drivers while the network satisfies user equilibrium conditions. Advanced Traveler Information Systems (ATIS) are becoming one of the most common traffic management tools that are used to improve the performance of both the transportation system itself, and individual travelers using this system. ATIS systems provide real-time information regarding incidents, route travel times, and traffic congestion to assist travelers in selecting their best departure time and route choices. Direct benefits of ATIS include reduced travel time and cost, improved safety, whereas indirect benefits are improved driving conditions, which help in reducing day-to-day stress and anxiety and more importantly increased travel time reliability. The exploration and development of dynamic trip choice models, which incorporate both route and departure time choices, has been spurred by the development of such ATIS systems. The success of these ATIS systems mainly depends on the availability of reliable models that reflect traveler response to route guidance or other traveler information. Moreover, these models of combined route and departure time choice should be able to capture the "day-to-day learning behavior" of drivers.
Dynamic aspects of departure-time choice behavior in a commuting system: theoretical framework and …
1985
The day-today dynamics of departure-time decisions of urban commuters and the underlying behavioral mechanisms determining user responses to dynamically varying time-dependent congestion patterns are addressed. A conceptual model is presented incorporating the boundedly-rational notion of an indifference band of tolerable schedule delay. The results of an experiment involving real commuters interacting daily within a simulated traffic corridor are examined, with particular emphasis on the dynamics of user behavior.
New technology and the modeling of risk-taking behavior in congested road networks
Transportation Research Part C: Emerging Technologies, 2004
Intelligent transport systems provide various means to improve traffic congestion in road networks. Evaluation of the benefits of these improvements requires consideration of commutersÕ response to reliability and/or uncertainty of travel time under various circumstances. Various disruptions cause recurrent or non-recurrent congestion on road networks, which make road travel times intrinsically fluctuating and unpredictable. Confronted with such uncertain traffic conditions, commuters are known to develop some simple decision-making process to adjust their travel choices. This paper represents the decision-making process involved in departure-time and route choices as risk-taking behavior under uncertainty. An expected travel disutility function associated with commutersÕ departure-time and route choices is formulated with taking into account the travel delay (due the recurrent congestion), the uncertainty of travel times (due to incident-induced congestion) and the consequent early or late arrival penalty. Commuters are assumed to make decision on the departure-time and route choices on the basis of the minimal expected travel disutility. Thus the network will achieve a simultaneous route and departure-time user equilibrium, in which no commuter can decrease his or her expected disutility by unilaterally changing the route or departure-time. The equilibrium is further formulated as an equivalent nonlinear complementarity problem and is then converted into an unconstrained minimization problem with the use of a gap function suggested recently. Two algorithms based on the Nelder-Mead multidimensional simplex method and the heuristic