ATIS-a modular approach (Advanced Traveler Information Systems)

This paper describes a compact driver interface (CDI) for an in-vehicle navigation and route guidance system. The CDI consists of a partially flexible display device, a voice output subsystem, and a driver input device. The display device, which may be mounted in the dashboard or the headliner, is spatially removed from the input device, which is mounted in or near the armrest. Despite its small size, the CDI can comfortably accommodate the features required for route guidance and is easy to operate.

This chapter, from a handbook on transportation systems and traffic control, presents a discussion on route guidance systems. After first defining route guidance and its many alternative forms, the authors next discuss stand-alone systems, including Internet-based systems and dispatching systems. A discussion on in-vehicle systems follows, in which the authors look at operator-assisted route guidance, autonomous route guidance, and dynamic route guidance systems, as well as discussing the benefits of in-vehicle systems. The authors conclude with a look at the future of route guidance.

Traffic signal controllers and route guidance systems are two major subsystems of Intelligent Transportation Systems that influence each other directly. Due to correlation between these subsystems, some research has been done to combine them into an integrated system. In this paper, PersianGulf - an autonomous combined traffic signal controller and route guidance system - will be proposed which has two unique features as compared to similar research. First, it is totally distributed because calculation of optimal routes are done independently in traffic signal controllers of intersections and second, it is totally autonomous because there is no need to communicate/cooperate with either traffic supervisors such as traffic management centers or the vehicles/drivers. Also, Simulation results show that PersianGulf improve the average speed of vehicles significantly in the simulated scenarios.

A Microscopic Traffic Simulator for evaluation of dynamic traffic management systems

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers' route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Route guidance system (RGS) is widely used as one of the main measures of Intelligent Transportation System, which means to maintain an acceptable service level for mobility of persons and goods. But the traffic benefits brought by RGS vary a lot with the characteristics of road networks and traffic flow. Then how to evaluate the traffic benefits is of importance to its implementation. The paper analyzed traffic benefits of RGS. Evaluation Measures of Effectiveness (MOEs) of RGS and its definition models are put forward. An application of the research results is performed with TESS (Tongji transportation nEtwork Simulation System). Comparison of outputs from TESS and VISSIM is provided, which proved the validity of the research. The research is important for evaluation of RGS and integration of RGS and other traffic management and regulation measures.

Route guidance systems in vehicles has started to use an oblique, slanted view of the map, mimicking something of the egocentric perspective the driver sees through the windscreen. Is this an effective strategy? What is the most effective map design to convey route guidance to drivers, and how can this be measured? In an experiment with four different modes of map displays the speed of decision making and accuracy of navigation have been tested. The four map types were: the traditional paper map, the northup electronic map with position plotting (the symbol of the vehicle moving in the static map), the head-up electronic map (map moving, the position of the vehicle static and facing up) and the egocentric view map display, a 3-D scenery mimicking the world outside the wind screen. The experiment showed clearly that the egocentric 3-D view was the most effective.

The Berlin field trial of a beacon-supported route guidance system started at the end of June 1989. System performance, acceptance by drivers, and benefits for users and the community from this new technology are investigated in a one-year test. 250 out of 1200 intersections with traffic lights and 10 locations on the freeway have been equipped with infrared beacons for communication with 700 specially equipped vehicles. LISB is based on a strategy that processes guidance information in a central computer system. Communication between in-vehicle units and roadside beacons allows for a continuous updating of traffic and route guidance information. An information system considering actual traffic conditions offers benefits to the drivers, even if they know their routes but do not know which of the alternatives is the quickest. Using two-way monologs for data exchange (instead of a dialog system) satisfies the strict German regulations on protection of personal data. Future applications of LISB are considered. >

Enhancing accessibility in transportation systems is an escalating interest among researchers, fueled by the rising issues with traffic congestion and technological innovations. Accessibility significantly contributes to improving the quality of life, thereby necessitating an examination of how route guidance systems impact it. This study explores the influence of system optimum (SO) and user equilibrium (UE) route guidance systems on accessibility, particularly in the context of connected autonomous vehicles (CAVs) embedded in the network. We employ a hybrid assignment model that enables the concurrent allocation of SO and UE assignments, along with the Gravity model to compute accessibility. The Sioux Falls network, previously leveraged in prior research, was chosen for numerical simulations to permit insightful comparisons. Our study scrutinizes three scenarios: the first scenario examines the repercussions of route guidance systems on human-driven vehicles, while the second and third assess their effects on CAVs. The difference between the second and third scenario is in the way of increasing the capacity in the assignment. Our findings reveal that at lower penetration rates of route guidance systems, accessibility initially dips, and then ascends. When all human-driven users adopt route guidance systems, accessibility increases by 12.75% compared to the initial state (without any intervention). Remarkably, should all users employ autonomous vehicles, accessibility would surge by 220% compared to the initial state (without any intervention). These insights highlight the importance of integrating CAVs and route guidance systems into transportation planning to enhance accessibility and improve the quality of life.

A decentralized data association is presented for 3D dead reckoning in a modular omnidirectional vehicle. Wheel units assembled into the vehicle partially estimates the vehicle position based on their own onboard sensors. The partial estimates are exchanged among the units and fused in a decentralized manner. Then the units can precisely identify the vehicle position. The dead reckoning algorithm is formulated based on the information filter and the Covariance Intersection to effectively integrate the partial estimates without taking into consideration their correlation. The unscented transformation is also exploited to predict the vehicle position without linearizing the nonlinear model related to the vehicle motion. The proposed method brings highly modularity and extendibility in the dead reckoning system. Experimental results validate that the proposed dead reckoning provides better positioning accuracy than the conventional Kalman filter-based method does.

A fault-tolerant dead reckoning system is presented for a modular vehicle, which consists of one chassis unit and several wheel units. The units locally estimate the vehicle position based on their own internal sensors. The local estimates are exchanged among the units via an inter-communication system, and they are fused in a decentralized manner. The units can then determine the vehicle position accurately. The decentralized dead reckoning algorithm is formulated based on the information filter and the covariance Intersection method. For enhancing the reliability of the dead reckoning a multi-model based fault detection and diagnosis (FDD) of the internal sensors is incorporated into the dead reckoning system. The units diagnose their sensors with the FDD system, and they apply only the normal sensors for the vehicle localization. In this paper two fault modes (hard fault and noise fault modes) of the sensors are handled; on the hard fault the sensor output is stuck at a constant value. On the noise fault it is disturbed by a large noise. The FDD algorithm is based on the variable structure interacting multiple-model estimator. The fault-tolerant dead reckoning algorithm was implemented on our indoor test-vehicle, which consists of one chassis unit and four wheel units. Experimental results show that our dead reckoning provided better localization accuracy than the conventional one (i.e., the dead reckoning without sensor FDD system) did even though the sensors partially failed.

This paper reviews key aspects of route guidance and information systems (RGISs), which disseminate to drivers messages with information and recommendations intended to assist their route choice decisions. It first summarizes the major features that characterize and distinguish different RGIS designs. An important distinction exists between non-predictive and predictive systems. In the former, guidance messages are based on measurements or estimates of prevailing network conditions, while, in the latter, messages are derived from forecasts of future conditions. For predictive systems, the key issue is consistency: ensuring that drivers’ reactions to guidance derived from forecasts do not invalidate those forecasts. It is shown that the determination of consistent guidance must be model based and can be formulated as a fixed-point problem. The modelling of driver response to guidance is a relatively new subject; the paper surveys the main issues, and relates these to the user-and system-level evaluation of RGISs. A final section identifies some areas of current research.

This paper reviews key aspects of route guidance and information systems (RGISs), which disseminate to drivers messages with information and recommendations intended to assist their route choice decisions. It first summarizes the major features that characterize and distinguish different RGIS designs. An important distinction exists between non-predictive and predictive systems. In the former, guidance messages are based on measurements or estimates of prevailing network conditions, while, in the latter, messages are derived from forecasts of future conditions. For predictive systems, the key issue is consistency: ensuring that drivers' reactions to guidance derived from forecasts do not invalidate those forecasts. It is shown that the determination of consistent guidance must be model based and can be formulated as a fixed-point problem. The modelling of driver response to guidance is a relatively new subject; the paper surveys the main issues, and relates these to the user-and system-level evaluation of RGISs. A final section identifies some areas of current research.

This technical report consists of a collection of task analytic data to support heavy vehicle driver assessment and protocol development. Data were collected from professional drivers to provide insights into the following issues: the meaning of the term workload to heavy vehicle drivers (N = 41 drivers interviewed); the demand placed on drivers (N = 55) by various driving conditions using a psychological scaling approach; the safety criticality and difficulty of selected standard driving tasks (N = 30 drivers assessed); the perceptual, motor, and cognitive loads imposed by various tasks while on the road under various conditions (N = 9 drivers observed over-the-road during revenue runs); and a preliminary analysis of the key features of generic high-technology in-cab devices. When professional truck drivers speak of workload, they tend to talk in terms of time stress or stress caused by delays to their schedules. Various driving conditions were unidimensionally scaled in terms of order of impact on drivers, and those results are presented and discussed. Safety criticality and difficulty assessment results provided some validation for the scaling outcomes. Visual allocation measures in this study suggested that the use of common in-cab tasks would serve as baseline measures, based on the revenue runs. Finally, key features considered in evaluating voice communication systems, vehicle navigation and route guidance systems, single/integrated displays, and text communication devices were determined.

This correspondence presents an optimal guidance algorithm which takes into account the driver's compliance to route advice. The optimal control consists of minimizing the travel time of guided vehicles using a queue-based model. The optimization is based on the use of forward dynamic programming and decomposition-coordination methods. Driver's compliance is modeled by a linear function of the guidance advice (control). An assessment of the criteria degradation induced by nonperfect compliance is performed. Results show that this degradation of the criteria is low in fluid traffic conditions but high in congested situations. Thus human factors cannot be neglected in route guidance systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>