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

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.

Feedback contents of previous information feedback strategies in advanced traveler information systems are almost real-time traffic information. Compared with real-time information, prediction traffic information obtained by a reliable and effective prediction algorithm has many undisputable advantages. In prediction information environment, a traveler is prone to making a more rational route-choice. For these considerations, a mean velocity prediction information feedback strategy (MVPFS) is presented. The approach adopts the autoregressive-integrated moving average model (ARIMA) to forecast short-term traffic flow. Furthermore, prediction results of mean velocity are taken as feedback contents and displayed on a variable message sign to guide travelers' route-choice. Meanwhile, discrete choice model (Logit model) is selected to imitate more appropriately travelers' route-choice behavior. In order to investigate the performance of MVPFS, a cellular automaton model with ARIMA is adopted to simulate a two-route scenario. The simulation shows that such innovative prediction feedback strategy is feasible and efficient. Even more importantly, this study demonstrates the excellence of prediction feedback ideology.

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.

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.

Suppose that in an urban transportation network there is a specific advanced traveler information system (ATIS) which acts for reducing the drivers' travel time uncertainty through provision of pre‐trip route information. Because of the imperfect information provided, some travelers are not in compliance with the ATIS advice although equipped with the device. We thus divide all travelers into three groups, one group unequipped with ATIS, another group equipped and in compliance with ATIS advice and the third group equipped but without compliance with the advice. Each traveler makes route choice in a logit‐based manner and a stochastic user equilibrium with multiple user classes is reached for every day. In this paper, we propose a model to investigate the evolutions of daily path travel time, daily ATIS compliance rate and yearly ATIS adoption, in which the equilibrium for every day's route choice is kept. The stability of the evolution model is initially analyzed. Numerical results obtained from a test network are presented for demonstrating the model's ability in depicting the day‐to‐day and year‐to‐year evolutions.

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.

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

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.

Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit

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.

As an important part of the Intelligent Transportation System (ITS), the information feedback strategy has drawn more and more scholars' attention. A variety of feedback strategies are proposed to improve the traffic efficiency. These strategies are based on simple route scenarios, but most route scenarios are always complex in reality. In this letter, based on a complex beltway scenario, we propose a new traffic information feedback strategy called Beltway Feedback Strategy (BFS). The simulation results show that the BFS can effectively improve the transportation ability of beltways.

The information feedback strategies for traffic guidance are important to improve the traffic capacity in Intelligent Transportation System. It assumed that all vehicles on the road are floating vehicles in the current information feedback strategies research. In reality, not all vehicles can be equipped with vehicular terminals, which are non-floating vehicles. We proposed a new strategy, Estimated Weighted Mean Velocity Feedback Strategy (EWMVFS), which can estimate the road status in the scenario with the coexistence of floating and non-floating vehicles. Simulation results show that EWMVFS can decrease the negative influence of the non-floating vehicles, which means better stability, guidance performance and practicability.

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

Stochastic Route Choice Equilibrium Assignment for Travelers with Heterogeneous Regret Aversions

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.