Modeling Risk-Taking Behavior in Queuing Networks with Advanced Traveler Information Systems

Modeling of the Market Penetration of Advanced Traveler Information Systems Using a Mixed Network Equilibrium Model

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.

The concepts of human information processing provide design recommendations for advanced traveler information systems (ATISs). A driver’s information processing model for the ATIS environment is proposed. This qualitative model explicitly depicts how drivers process ATIS information, from the moment it is received until a decision is made in response to the information. The model is supported by data collected with a driving simulator. The results of the driving simulator study indicate that, because of the limitations of human memory, drivers prefer to have short, simple ATIS information conveyed to them whenever the circumstance allows. Furthermore, drivers do not always need or want visually displayed information (e.g., an electronic map) to assist them in route navigation. In unfamiliar traffic networks, a motorist’s propensity to change routes is strongly correlated to the quality of ATIS information being processed. With the right amount of traffic information presented using an effective conveyance method, more drivers are likely to accept and use ATIS information and divert to alternate routes. The challenge for ATIS designers is to provide options that are acceptable to ATIS users.

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

The objective of this paper is to examine the assumption of "uniform market penetration" embedded in the existing evaluation studies of Advanced Traveler Information Systems (ATIS), and to show that this assumption might possibly lead to underestimation of the benefits derived from ATIS. The assumption is then relaxed by endogenising market penetration of ATIS as a function of information benefit using a mixed equilibrium traffic assignment model. It is observed from the numerical experiments that the information benefit and the market penetration of ATIS depend strongly on the trip characteristic such as trip length; ignoring such an important factor may give rise to misleading results of ATIS performance.

An optimal global algorithm for route guidance in advanced traveler information systems

With the rapid development of urban traffic, travel time had become the crucial factor for road-user to make decisions on means of transportation, path finding. Besides, emerging information technology, such as Traffic information technology, GIS (Geographic Information System) technology and etc, that have promoted the tendency that people are inclining to plan personal travel through Dynamic Route Guidance System characterized as travel time prediction according to real-time traffic data. In this paper, it first proposed the factors of selecting the test O-D (original-destination), which travel time will be predicted by GoogleMap, BaiduMap, SogouMap and SOSOMap, for comparison, probe vehicle would run along the route provided by those online ATIS (advanced traveler information system) to check the accuracy of travel time predicted. Finally, it analyzes and assesses the service of travel time prediction of such systems.

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.

An evaluation study of the Smart Traveler Automated Ridematching Service is reported. This advanced traveler information system (ATIS) demonstration used a fully automated Touch-Tone telephone system to offer real-time ride matching service for just 1 day or for conventional carpool arrangements. An innovative feature was an automated messaging system in which a computer was used to dial up and leave recorded messages with potential matches. The system failed to perform as intended, and problems were experienced with the generation of computer-based reports for monitoring purposes. The demonstration revealed a low level of use and a mismatch with the needs of the market. Lessons are drawn for future ATIS initiatives.

Based on analytical and simulation methods, this paper discusses the path choice behavior of mixed traffic flow with autonomous vehicles, advanced traveler information systems (ATIS) vehicles and ordinary vehicles, aiming to promote the development of autonomous vehicles. Firstly, a bi-level programming model of mixed traffic flow assignments constrained by link capacity is established to minimize travel time. Subsequently, the algorithm based on the incremental allocation method and method of successive averages is proposed to solve the model. Through a numerical example, the road network capacity under different modes is obtained, the impact of market penetration on travel time is analyzed, and the state and characteristics of single equilibrium flow and mixed equilibrium flow are explored. Analysis results show that the road network can be maximized based on saving travel time when all vehicles are autonomous, especially when the autonomous lane is adopted. The travel time can be shortened by increasing the market penetration of autonomous vehicles and ATIS vehicles, while the former is more effective. However, the popularization of autonomous vehicles cannot be realized in the short term; the market penetration of autonomous vehicles and ATIS vehicles can be set to 0.2 and 0.6, respectively, during the introduction period.

Accurate estimation of travel times from single-loop detectors

Travel time is an important parameter in evaluating the operating efficiency of traffic networks, in assessing the performance of traffic management strategies, and as input to many intelligent transportation systems applications such as advanced traveler information systems. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as inductance loop detectors. Because of the widespread deployment of loop detectors, they are one of the most widely used inputs to travel time estimation techniques. There are different methods available to calculate the travel time from loop detector data, such as extrapolation of the point speed values, statistical methods, and models based on traffic flow theory. However, most of these methods fail during the transition period between the normal and congested flow conditions. The present study proposes several modifications to an existing traffic flow theory based model for travel time estimation on freeways, such that the model can estimate travel time for varying traffic flow conditions, including transition period, directly from the loop detector data. Field data collected from the I-35 freeway in San Antonio, Tex., USA, are used for illustrating the results. Automatic vehicle identification data collected from the same location are used for validating the results. Simulated data using CORSIM simulation software are also used for the validation of the model.

Emissions Impact of Road Traffic Incidents Using Advanced Traveller Information Systems in a Regional Scale

Decision reliability is becoming an important issue for both researchers and transportation service providers; different models and methods have been proposed to improve decision reliability. A method is proposed for improving decision reliability by analyzing the moments and central moments of historical travel time data. Because of the existence of traffic congestion—especially nonrecurrent traffic congestion—travel time is generally a random variable with an asymmetric historical distribution. Statistical methods are applied to obtain the higher-order moments and central moments of historical travel time, which provide quantitative information about the variability, asymmetry, and leptokurtosis of travel time. This information is useful in making reliable decisions and avoiding scenarios that can potentially lead to extremely high costs. The method is based on advanced traveler information systems, which provide useful historical travel time information for traffic networks. The new method and other me...

The Paso Del Norte region includes El Paso County (Texas), Dona Ana County (New Mexico), and Ciudad Juarez (Mexico) along the southwest border region between the United States and Mexico. With a combined population of more than 2 million, the city of El Paso (Texas) and Ciudad Juarez have an intertwined transportation system with one of the nation's busiest international borders. In response to growing transportation needs in this binational region, the Texas Transportation Institute established a new transportation research center in the city of El Paso. One of the many research initiatives identified at the early stages of the center's inception was to develop a mobility information system for the Paso Del Norte region. The goals of this regional mobility information system were to demonstrate the significance and benefits of archiving intelligent transportation system (ITS) data, build a centralized repository of transportation data, provide the ability to monitor regional and cross-border mobility, and provide a platform to adopt newer ITS technologies. The objectives of the regional mobility information system are to provide archived transportation data user services and an advanced traveler information system in the region. The regional mobility information system is the first of its kind in the region and has been well received by stakeholder agencies. The system has an extremely flexible and scalable database framework and uses the concept of data warehouse and database-centric architecture. As an advanced traveler information system, the system provides access to users’ information related to everyday travel through a single-window website.