Mean Velocity Prediction Information Feedback Strategy in Two-Route Systems under ATIS

Day-To-Day Route Choice Decision Simulation Based on Dynamic Feedback Information

Speech recognition technology has potential for application to intelligent transportation systems (ITS) in the area of advanced traveler information systems (ATIS). ATIS areas which can greatly benefit from speech recognition are: pre-trip traffic information; en-route traffic information; route guidance; ride matching and reservation; transit information services; and traffic and transit management control centers. This paper concentrates on telephony applications of speech recognition to two ATIS applications, real-time traffic information and public transit information. We first discuss the general considerations involved in development of a multiline speech interface to a traffic or transit database, including system architecture and sizing, and the development of dialogs and grammars which are user friendly and efficient. We then discuss two prototype applications of this technology using BBN's HARK speaker-independent speech recognition product. The first is to SmartRoute System's SmarTraveler traveler information system, which was an FHWA Operational Test, and the second to Montgomery County Maryland's public transit system. These were small scale tests to help us develop the technology required for full-scale implementations, but showed that the speech interface was practical, and provided much improved ease of use and performance over conventional touch tone based interactive voice response (IVR) systems.

This paper describes a dynamic model for evaluating the benefit of an Advanced Traveler Information System (ATIS). The model consists of five modules, including a route choice module and a traffic simulation module. The route choice module considers different categories of drivers’ route choice behaviors with different levels of access to traffic information. The traffic simulation module simulates dynamic network conditions in response to traffic information. The dynamic model allows the investigation of a network-wide performance in a road network under different traveler information systems. Some results are provided to demonstrate the use of the model. They suggest that providing traffic information can reduce the total network delay. However, there is an optimum level of market penetration beyond which real-time information can no longer reduce delay of the network and the individual.

On stochastic transport network, travelers tend to reserve travel time budget (TTB) for every alternative route, to assure their on-time arrive with a predefined confidence level. This paper incorporates the concept of TTB into a deterministic nonlinear dynamic system, to model the day-to-day evolution process of travelers' risk-taking route choices on stochastic degradable road network. This paper also introduces consistent traffic information prediction, which can be provided by Advanced Traveler Information Systems (ATIS), into the dynamic system to investigate its influence on travelers' route choice behaviors and the corresponding day-to-day network flow evolutions. Illustrative examples are presented to demonstrate that, the consistent predictive traffic information can help to stabilize day-to-day network flow fluctuations, while travelers' extreme risk-averse route choice behaviors, on the other hand, would cause greater network flow fluctuations.

As a subsystem of an Intelligent Transportation System (ITS), an Advanced Traveller Information System (ATIS) disseminates real-time traffic information to travellers. To help travellers better make their route choice decisions, there is a strong need to predict traffic congestion and disseminate the predicted congestion information to travellers. This paper offers some insights and predictions on how ATIS information provision is becoming more pervasive due to recent advances in telecommunication systems. The paper also discusses how ATIS systems will likely evolve based on the experiences of Information Service Providers (ISP) and ATIS modelling specialists. Then, it reviews four types of prediction models: 1) simulation models; 2) dynamic traffic assignment (DTA) models; 3) statistical models; and 4) heuristic models. The functional requirements and capabilities of the four types of prediction models are discussed and summarised. Furthermore, a comprehensive prediction procedure is presented, which combines the four types of prediction models.

Modeling the benefits of advanced traveler information systems in corridors with incidents

Common bus route navigation system provides distance and transferring information based on the user-determined start point and end point on the map. This paper presents the research and experiment of an advanced bus route navigation system in Beijing, which can additionally provide travel time information from start to end. The travel time is predicted based on statistical and real-time traffic information. In Beijing, about 10000 taxies send GPS data to a traffic information center to generate real-time traffic data, and the center accumulates the historical traffic data for generating statistical database. Field test of our system was carried out in January 2007, and the experiment result shows the real-time and statistical traffic information can be used not only for car navigation system, but also for public transit service. In our experiment, the average error rate of predicted travel time is about 10%, which is comparable to car navigation system.

An application of cumulative prospect theory to freeway drivers’ route choice behaviours

Effect of real-time traffic information on the relaxation process is studied. The provision of traffic information on a real-time basis to individual vehicles is expected as an effective way to distribute the traffic flow to a network of roads. However, it has been also pointed out that real-time information prevents the distribution of the traffic flow on a network of roads because of the concentration of the traffic flow to the recommended route. We can see that the real-time traffic information based on averaged velocity or travel time give rise to fluctuation with chaotic structure on the relaxation process of traffic flow formed by cellular automata. It is also proposed that the real-time traffic information composed by differentiation of averaged velocity of vehicle is effective to reduce the fluctuation of traffic flow.

The research of driver?s route choice behavior helps to increase utility of existed route network (especially city traffic network), and improve traffic conditions. From the point of macroscopic view, through discussing the time evolution pattern of driver's route choice behavior with the influence of dynamic traffic information upon the pattern, it is proved that the behavior of route choice is uncertain. So in designing traffic guiding system or traffic information system, the influence of disturbing factor should be taken into consideration to guarantee a balanced dynamic traffic distribution of the system.

The purpose of this paper is to develop a real-time planning algorithm for making possible a more proper description of drivers' route choice behavior and processes under real time traffic information in a traffic network. The algorithm uses concepts from Decision Field Theory (DFT) and Bayesian belief network (BBN). DFT is widely known in the field of mathematical psychology and is used to study drivers' multi-attribute cognition and decision process under real time traffic information. BBN is employed to infer updating of road condition under real time traffic information. Using this algorithm, a driver develops his route dynamically until he reaches his preferred destination. The preferences of routes which are directly accessible from the current position are obtained via BBN and DFT. Critical factors that affect drivers' response to real time traffic information are quantitatively studied through interactive simulation and from the viewpoint of cognitive psychology.

It is a rational assumption that travelers would not be completely in compliance with the guidance by advanced traveler information systems (ATIS). This paper presents a dynamic programming model for investigating the day-to-day receiving and adjusting process to the information released by ATIS. The dynamic model aims at seeking an optimal information release strategy for minimizing the overall disutility and a logit-based route choice is given on the base of adjusted travel times by travelers. The model is also an optimal control model and solved by solving a series of sequential programs. Numerical results are presented to show that there exists an optimal information release strategy that can drive the flow pattern to a system optimum.

Switching threshold of commuting Travelers’ route choice under different patterns of information

Because of shortage of raw materials, the energy-saving issues become popular in Vehicular Ad-hoc Networks (VANETs). A VANET is formed by traveling vehicles with communicating capability and thus it brings various applications. Navigation system is one of important applications in VANETs. The traditional navigation system usually plans a shortest path for users according to geographic maps but the planned path may become a slower one due to the traffic congestion. A congestion path not only delays the traveling time but wastes fuel. So the real-time traffic information should be considered while constructing a navigation path. In this paper, we propose a navigation system to find a fuel-saving navigation path by considering real-time traffic information. In order to estimate the traffic load on the future path, we maintain a table of historical traffic information by Intelligent Transport Systems (ITS). Furthermore, our system can dynamically change the navigation path by updating traffic information. In the simulation results, our approach can save more than 20% fuel consumption when vehicles travel in a traffic congestion environment.

We investigate the effects of four different information feedback strategies on the dynamics of traffic, travelers' route choice and the resultant system performance in a signal controlled network with overlapped routes. Simulation results given by the cellular automaton model show that the system purpose-based mean velocity feedback strategy and the congestion coefficient feedback strategy have more advantages in improving network utilization efficiency and reducing travelers' travel times. The travel time feedback strategy and the individual purposed-based mean velocity feedback strategy behave slightly better to ensure user equity.